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AAUW deeply appreciates <strong>the</strong> following generous contributors for <strong>the</strong>ir support of this research <strong>report</strong>.AAUW gratefully acknowledges <strong>the</strong> financial support of <strong>the</strong> National ScienceFoundation, Research on Gender in Science and Engineering award 1420214, for<strong>the</strong> production and dissemination of this <strong>report</strong>. Any opinions, findings, and conclusionsor recommendations expressed in this material are those of <strong>the</strong> authors anddo not necessarily reflect <strong>the</strong> views of <strong>the</strong> National Science Foundation.AAUW gratefully acknowledges AT&T, <strong>the</strong> exclusive Information andCommunications Technology Member of AAUW’s STEM Workforce Coalition.AT&T’s leadership support will help <strong>the</strong> <strong>report</strong> inspire maximum impact among<strong>the</strong> broadest audience possible. The views expressed in this <strong>report</strong> do notnecessarily reflect <strong>the</strong> views of AT&T.AAUW gratefully acknowledges <strong>the</strong> members of <strong>the</strong> Mooneen Lecce GivingCircle for <strong>the</strong>ir generous support and encouragement of AAUW’s research for <strong>the</strong>past eight years. Their work honors <strong>the</strong> legacy of Mooneen Lecce, whose passion forAAUW’s mission continues to inspire volunteerism and charitable giving dedicatedto improving <strong>the</strong> lives of women and girls.
ContentsForewordAcknowledgmentsExecutive SummaryChapter 1. Women in Engineering and ComputingChapter 2. Why So Few?Chapter 3. Gender Bias and EvaluationsChapter 4. Gender Bias and Self-ConceptsChapter 5. Stereotype Threat in <strong>the</strong> WorkplaceChapter 6. Making <strong>the</strong> World a Better PlaceChapter 7. College Environment and CurriculumChapter 8. Persistence and Sense of FitChapter 9. A Workplace for EveryoneChapter 10. What Can We Do?AppendixReferencesixxi17334755636975859197111128FiguresFigure 1. Women in Selected STEM Occupations, 1960–2013Figure 2. Engineering Workforce, by Gender and Race/Ethnicity, 2006–2010Figure 3. Computing Workforce, by Gender and Race/Ethnicity, 2006–2010Figure 4. Women in Engineering, Computing, and Selected O<strong>the</strong>r Occupations, 2013Figure 5. Intent of First-Year College Students to Major in STEM Fields,by Gender, 2014Figure 6. Bachelor’s Degrees Earned by Women, Selected Fields, 1970–2013Figure 7.Figure 8.Figure 9.Figure 10.Bachelor’s Degrees Awarded in Engineering, by Disciplineand Gender, 2013Bachelor’s Degrees Awarded in Computing, by Genderand Discipline, 2013Population Ages 20–24 and Bachelor’s Degrees Awarded in Selected Fields,by Race/Ethnicity and Gender, 2013Student Population from High School to Doctorate in Engineering orComputing, by Race/Ethnicity and Gender, 2012Figure 11. Retention in Engineering, by Gender, 2010Figure 12. Retention in Computing, by Gender, 2010999141819202123242727
Figure 13. Female Faculty, by Rank and Discipline, 2004 and 2013Figure 14.Figure 15.Figure 16.Figure 17.Figure 18.Figure 19.Figure 20.Figure 21.Figure 22.Figure 23.Figure 24.Figure 25.Faculty Ratings of Lab Manager Applicant, by Gender of ApplicantProbability of Selecting <strong>the</strong> Best Candidate for a Ma<strong>the</strong>matical TaskAverage Science-Male Implicit Association Test Score,by Gender and College MajorAverage Science-Male Explicit Association Score, by Genderand College MajorResearch Conversations with Male Colleagues and Level of JobDisengagement, by GenderSocial Conversations with Male Colleagues and Level of JobDisengagement, by GenderPerceived Communal and Agentic Goal Fulfillment, by Type of CareerCareer Interest as a Function of Endorsement of Communal GoalsAverage Goal Endorsement, by GenderFemale Computer Science Graduates Nationally and at Harvey MuddCollege, by Graduation Year, 2000–2014Factors Contributing to Harvey Mudd College Students’ Choice to Majorin Computer Science, by GenderFemale Engineers’ Experience of Incivility at Work, by Level ofJob Satisfaction28495257596666717272778194Appendix FiguresFigure A1.Figure A2.Engineering OccupationsComputing OccupationsFigure A3. Pay Gap in Selected Engineering and Computing Occupations, 2013Figure A4. Associate Degrees Earned by Women, Selected Fields, 1990–2013Figure A5.Engineering and Computing Bachelor’s Degrees, by Race/Ethnicityand Gender, 2013Figure A6a. Engineering Bachelor’s Degrees Awarded to Women at <strong>the</strong> 25 LargestU.S. Engineering Schools, 2012Figure A6b. Engineering Bachelor’s Degrees Awarded to Women by U.S. EngineeringSchools with <strong>the</strong> Highest Representation of Women, 2012Figure A6c. Computing Bachelor’s Degrees Awarded to Women at <strong>the</strong> 25 LargestU.S. Computing Schools, 2012Figure A6d. Computing Bachelor’s Degrees Awarded to Women at U.S. ComputingSchools with <strong>the</strong> Highest Representation of Women, 2012Figure A7. Computing Occupations, Current (2012) and Projected (2022)Employment112112113114115116117118119120
AAUWixForewordDuring <strong>the</strong> 2014 White House Science Fair, President Barack Obama used a sportsmetaphor to explain why we must address <strong>the</strong> shortage of women in science, technology,engineering, and ma<strong>the</strong>matics (STEM), particularly in <strong>the</strong> engineering and computingfields: “Half our team, we’re not even putting on <strong>the</strong> field. We’ve got to change thosenumbers.”AAUW has been a leader in efforts to promote women in STEM since our founding in1881. Through fellowships, programs, advocacy, and research, AAUW has inspired hundredsof thousands of girls to pursue science and math and helped thousands of womenbecome scientists, engineers, and ma<strong>the</strong>maticians. AAUW’s 2010 research <strong>report</strong>, WhySo Few? Women in Science, Technology, Engineering, and Ma<strong>the</strong>matics, sparked nationwideinterest in <strong>the</strong> shortage of women in STEM, leading to new initiatives in schools, colleges,and government. Indeed, significant progress has been made in fields such as biologyand chemistry; yet in engineering and computing, women remain a distinct minority.<strong>Solving</strong> <strong>the</strong> <strong>Equation</strong>: The Variables for Women’s Success in Engineering and Computingfocuses on <strong>the</strong> underrepresentation of women in engineering and computing and providespractical ideas for educators and employers seeking to foster gender diversity. From newways of conceptualizing <strong>the</strong> fields for beginning students to good management practices,<strong>the</strong> <strong>report</strong> recommends large and small actions that can add up to real change.Engineering and computing are too important for women to be less than fully represented.Diversity in <strong>the</strong>se fields can contribute to creativity and productivity and, ultimately,to greater innovation. Join AAUW in our efforts to empower women and girls tosucceed in every field of endeavor.Patricia Fae HoAAUW PresidentLinda D. Hallman, CAEAAUW Executive Director
Executive Summary
AAUW 3also offered a higher salary to <strong>the</strong> male candidate.Ano<strong>the</strong>r study highlighted in chapter 3 found thatpotential employers systematically underestimated<strong>the</strong> ma<strong>the</strong>matical performance of women comparedwith men, resulting in <strong>the</strong> hiring of lowerperformingmen over higher-performing womenfor ma<strong>the</strong>matical work. Once objective past-performanceinformation was introduced, however, <strong>the</strong>employers made less biased hiring decisions. Biasis prevalent, but its effects can be diminished withmore comprehensive information.Hundreds of studies have documented <strong>the</strong>power of stereotypes to influence performancethrough a phenomenon known as “stereotypethreat.” Stereotype threat occurs when individualsfear that <strong>the</strong>y will confirm a negative stereotypeabout a group to which <strong>the</strong>y belong. One suchgroup is “women.” When negative stereotypesabout women’s ma<strong>the</strong>matical abilities are broughtto test-takers’ attention during tests, women’s performancedrops. Stereotype threat has been <strong>the</strong>orizednot only to influence women’s ma<strong>the</strong>maticalperformance but also to contribute to disengagementfrom fields in which women are negativelystereotyped, such as engineering and computing.Much research has been done on how stereotypethreat can affect academic performance, butresearchers are only recently beginning to examinehow stereotype threat affects women in <strong>the</strong>workplace. One finding in this area, highlightedin chapter 5, showed that <strong>the</strong> more often femaleSTEM faculty had research-related conversationswith <strong>the</strong>ir male colleagues, <strong>the</strong> less engaged <strong>the</strong>yfelt with <strong>the</strong>ir work. In contrast, <strong>the</strong> more socialconversations female STEM faculty had with <strong>the</strong>irmale colleagues, <strong>the</strong> more engaged <strong>the</strong>y <strong>report</strong>edbeing with <strong>the</strong>ir work. One possible explanationfor this finding is that research-related conversationswith male colleagues may generate stereotypethreat for female scientists. Social conversationswith male colleagues, on <strong>the</strong> o<strong>the</strong>r hand, may lessen<strong>the</strong> threat by increasing a feeling of belonging in<strong>the</strong>ir work environment. Research suggests that stereotypesare activated for women more frequentlywhen few women work in an organization. Thepresence of women at all levels of an organizationhas <strong>the</strong> potential to create environments that areless threatening for women.Gender biases affect not only how we view andtreat o<strong>the</strong>rs but also how we view ourselves andwhat actions we take as a result. From early childhoodwe are exposed to stereotypes that guide ourchoices and behavior in powerful and often invisibleways, steering us toward certain careers andaway from o<strong>the</strong>rs. As early as first grade, childrenhave already developed implicit biases associatingmath with boys. Studies suggest that girls whomore strongly associate math with boys and menare less likely to perceive <strong>the</strong>mselves as being interestedin or skilled at math and less likely to spendtime studying or engaging with math concepts.A recent analysis of international differencesin <strong>the</strong> composition of engineering and computingfields makes clear that <strong>the</strong> surrounding culturemakes a difference in <strong>the</strong> gender makeup of <strong>the</strong>sefields. Women in <strong>the</strong> United States earn approximatelya fifth of all computing degrees, whereas inMalaysia women earn about half of all computingdegrees. Similarly, in <strong>the</strong> United States womenearn fewer than a fifth of engineering degrees.In Indonesia, however, women earn almost halfof engineering degrees, and in a diverse groupof countries women account for about a third ofrecent engineering graduates.A study described in chapter 4 finds that mostmen who major in engineering and computing haverelatively strong implicit biases associating menwith science, whereas <strong>the</strong>ir female counterparts tendto have relatively weak science-male implicit biases.Engineering and computing workplaces have awider gap in <strong>the</strong> gender-science bias among femaleand male employees relative to o<strong>the</strong>r fields. Femalerole models in engineering and computing can helpshift implicit biases for both women and men.Emphasizing socialrelevanceOne factor that may contribute to girls and womenchoosing to pursue fields o<strong>the</strong>r than engineeringand computing is <strong>the</strong> small but well-documentedgender difference in desire to work with and help
4SOLVING THE EQUATIONo<strong>the</strong>r people. Although communal goals are widelyvalued by both women and men, research describedin chapter 6 finds that women are more likely thanmen to prioritize helping and working with o<strong>the</strong>rpeople over o<strong>the</strong>r career goals. Engineering andcomputing jobs clearly can provide opportunitiesfor fulfilling communal goals, but jobs in <strong>the</strong>sefields are not generally viewed that way. Ra<strong>the</strong>r,engineering and computing are often thought ofas solitary occupations that offer few opportunitiesfor social contribution. The perception and, in somecases, <strong>the</strong> reality that engineering and computingoccupations lack opportunities to work with andhelp o<strong>the</strong>rs may in part explain <strong>the</strong> underrepresentationof women in <strong>the</strong>se fields. Incorporatingcommunal aspects—both in messaging and in substance—intoengineering and computing work willlikely increase <strong>the</strong> appeal of <strong>the</strong>se fields to communallyoriented people, many of whom are women.Cultivating a senseof belongingPerhaps because of this combination of stereotypes,biases, and values, women often <strong>report</strong> that<strong>the</strong>y don’t feel as if <strong>the</strong>y belong in engineering andcomputing fields. A study highlighted in chapter 8found that female engineering students were lesslikely than <strong>the</strong>ir male counterparts to feel a strongsense of fit with <strong>the</strong> idea of “being an engineer”as early as <strong>the</strong>ir first year in college. This moretenuous sense of fit with <strong>the</strong> professional role of anengineer was found to be associated with a greaterlikelihood of leaving <strong>the</strong> field. By emphasizing <strong>the</strong>wide variety of expertise necessary to be a successfulengineer or computing professional—includingless stereotypically masculine skills such as writing,communicating, and organizing—college engineeringand computing programs can help youngwomen see engineering and computing as fields inwhich <strong>the</strong>y belong.Changing <strong>the</strong> environmentPast decades have shown that simply trying torecruit girls and women into existing engineeringand computing educational programs andworkplaces has had limited success. Changing <strong>the</strong>environment in college and <strong>the</strong> workplace appearsto be a prerequisite for fully integrating womeninto <strong>the</strong>se fields.CollegeHarvey Mudd College is a prime example of howchanging structures and environments can resultin a dramatic increase in women’s representationin computing. With leadership from <strong>the</strong> collegepresident and college-wide support, Harvey Muddincreased <strong>the</strong> percentage of women graduatingfrom its computing program from 12 percentto approximately 40 percent in five years. Thisdramatic increase was accomplished through threemajor changes: revising <strong>the</strong> introductory computingcourse and splitting it into two levels dividedby experience, providing research opportunitiesfor undergraduates after <strong>the</strong>ir first year in college,and taking female students to <strong>the</strong> Grace HopperCelebration of Women in Computing conference.These changes can be modified and applied at o<strong>the</strong>rcolleges and universities. Taken toge<strong>the</strong>r, <strong>the</strong>y providea roadmap for reversing <strong>the</strong> downward trendin women’s representation among bachelor’s degreerecipients in computing.The workplaceWhile many studies have focused on factorscontributing to women entering STEM occupations,far fewer have looked at <strong>the</strong> arguably equallyimportant question of why women leave <strong>the</strong>sefields, often after years of preparation, and whatfactors support <strong>the</strong>m in staying. The research featuredin chapter 9 sheds light on why some womenleave <strong>the</strong> engineering workforce and why o<strong>the</strong>rsstay. Women who leave engineering are very similarto women who stay in engineering. The differences<strong>the</strong> researchers found were not in <strong>the</strong> women<strong>the</strong>mselves but in <strong>the</strong>ir workplace environments.Women who left engineering were less likelyto have opportunities for training and development,support from co-workers or supervisors, andsupport for balancing work and nonwork roles thanwere women who stayed in <strong>the</strong> profession. Femaleengineers who were most satisfied with <strong>the</strong>ir jobs,
in contrast, worked for organizations that providedclear paths for advancement, gave employeeschallenging assignments that helped develop andstreng<strong>the</strong>n new skills, and valued and recognizedemployees’ contributions.Women are making significant contributions to<strong>the</strong> fields of engineering and computing yet are stilla distinct minority in <strong>the</strong>se fields. Stereotypes andbiases lie at <strong>the</strong> core of <strong>the</strong> challenges facing womenin engineering and computing. Educational andworkplace environments are dissuading women whomight o<strong>the</strong>rwise succeed in <strong>the</strong>se fields. Expandingwomen’s representation in engineering and computingwill require concerted effort by employers, educationalinstitutions, policy makers, and individualsto create environments that are truly welcoming forwomen.AAUW 5
Chapter 1.Women in Engineeringand Computing
8SOLVING THE EQUATIONIn recent years <strong>the</strong> fields of science, technology,engineering, and ma<strong>the</strong>matics—collectively knownas STEM—have received much attention for <strong>the</strong>ircritical role in maintaining our nation’s competitiveedge in <strong>the</strong> global economy. Although <strong>the</strong> STEMfields are often grouped toge<strong>the</strong>r, important differencesexist among <strong>the</strong>m. In particular, engineeringand computing stand out as <strong>the</strong> STEM fields thatoffer <strong>the</strong> best opportunities for <strong>the</strong> greatest numberof people, accounting for more than 80 percentof STEM jobs (Landivar, 2013) as well as offeringa higher return on educational investment. Yetwomen remain less well represented in engineeringthan in any o<strong>the</strong>r STEM field, 1 and computing has<strong>the</strong> dubious distinction of being <strong>the</strong> only STEMfield in which women’s representation has steadilydeclined throughout <strong>the</strong> past few decades. In 2013just 12 percent of engineers and 26 percent of computingprofessionals were women (AAUW analysisof U.S. Department of Labor, Bureau of LaborStatistics, 2014b). Black and Hispanic women areeven more underrepresented compared with <strong>the</strong>irrepresentation in <strong>the</strong> general population than arewomen overall, making up just 4 percent of computingprofessionals and fewer than 2 percent ofengineers (AAUW analysis of U.S. Census Bureau,2011a).This set of circumstances motivated AAUW toexamine <strong>the</strong> latest research on <strong>the</strong> factors behind<strong>the</strong> persistent underrepresentation of womenin <strong>the</strong> U.S. engineering and computing workforce.AAUW chose to focus on engineering andcomputing toge<strong>the</strong>r because of important commonalitiesbetween <strong>the</strong>se two fields, including <strong>the</strong>quantitative nature of <strong>the</strong> work and <strong>the</strong> masculineculture in both fields. Reviewing research on <strong>the</strong>underrepresentation of women in engineeringand computing allowed AAUW to draw on alarger body of research that is often relevant forboth fields. This chapter explores <strong>the</strong> dimensionsof women’s underrepresentation in <strong>the</strong>se fields,chapter 2 discusses <strong>the</strong> reasons behind women’sunderrepresentation, chapters 3 through 9 describespecific research findings in detail, and chapter 10provides recommendations for change. Overall <strong>the</strong><strong>report</strong> aims to increase understanding of women’sunderrepresentation and suggest ways in whichwomen’s participation can be increased.Women’s persistentunderrepresentation inengineering and computingIn <strong>the</strong> past 50 years women have entered <strong>the</strong>workforce in record numbers, making <strong>the</strong>ir wayinto many fields previously dominated by men. Butwomen’s gains have been more modest in engineeringand computing than in o<strong>the</strong>r historically maleprofessions, such as law, business, and medicine.Women made up just 3 percent of lawyers andjudges in 1960, but women’s representation inthose jobs had increased to 33 percent by 2013.Likewise among physicians and surgeons, womenmade up just 7 percent of professionals in 1960, butthat number had grown to 36 percent by 2013. Inmanagement occupations women held 14 percentof jobs in 1960 and 38 percent in 2013 (AAUWanalysis of U.S. Census Bureau, 1963, and U.S.Department of Labor, Bureau of Labor Statistics,2014d).Women have also made substantial gains inmany STEM disciplines. For instance, womenmade up 8 percent of chemists in 1960 butaccounted for 39 percent of <strong>the</strong> chemistry workforceby 2013 (see figure 1). In biology, women’srepresentation increased from just over a quarter tojust over half of <strong>the</strong> workforce during this period.On <strong>the</strong> o<strong>the</strong>r hand, women’s representation incomputing declined from just over a third of workersin 1990 to just over a quarter in 2013, about<strong>the</strong> same as it was in 1960. In engineering, womenmade up less than 1 percent of workers in 1960,growing to 12 percent of <strong>the</strong> workforce by 2013.Indeed, engineering has been described as <strong>the</strong> mostsex-segregated nonmilitary profession in <strong>the</strong> world(Cech, Rubineau et al., 2011; Charles & Bradley,2009).Figure 2 shows that, in recent years, whitewomen have made up 8 percent, Asian and PacificIslander women have made up 2 percent, and blackand Hispanic women have each made up 1 percentof <strong>the</strong> U.S. engineering workforce (see figure A1 in<strong>the</strong> appendix for <strong>the</strong> list of occupations includedin <strong>the</strong> engineering workforce). Overall, for bothwomen and men, more than three-fourths ofengineering workers in <strong>the</strong> United States werenon-Hispanic white, 13 percent were Asian and
10SOLVING THE EQUATIONPacific Islander, 5 percent were Hispanic, and 4percent were black (some of <strong>the</strong>se percentages donot match figure 2 exactly because of roundingcorrections).Figure 3 shows that in computing, whitewomen made up 17 percent, Asian and PacificIslander women made up 4 percent, black womenmade up 3 percent, and Hispanic women made up1 percent of <strong>the</strong> workforce (see figure A2 in <strong>the</strong>appendix for <strong>the</strong> list of occupations included incomputing). Overall, for both women and men, 69percent of computing workers were non-Hispanicwhites, 17 percent were Asian and Pacific Islander,7 percent were black, and 6 percent were Hispanic(some of <strong>the</strong>se percentages do not match figure 3exactly because of rounding corrections).Diversity in high-techcompaniesSome technology companies have for years madepublic <strong>the</strong> gender and racial/ethnic makeup of<strong>the</strong>ir workforce (see Intel Corporation, 2014, forexample). O<strong>the</strong>r prominent companies, such asGoogle, Apple, and Facebook, recently made publicfor <strong>the</strong> first time <strong>the</strong> racial/ethnic diversity of <strong>the</strong>iremployees as well as <strong>the</strong> percentage of <strong>the</strong>ir technicalemployees who are women. With 17 percent ofits technical workforce made up of women, Google(2014a) freely admitted, “We are not where wewant to be when it comes to diversity.” Apple (2014)<strong>report</strong>ed that women account for 20 percent of itstechnical workforce. At Facebook (2014), womenmake up just 15 percent of technical workers, and atTwitter (2014) only 10 percent of technical workersare women. O<strong>the</strong>r prominent high-tech companies<strong>report</strong> similar numbers. These recent efforts towardtransparency are an important step in addressing<strong>the</strong> problem and tracking future progress.Why women’srepresentation mattersWhy does women’s representation in engineeringand computing fields matter? The answer can besummed up in one word: innovation. Finding solutionsto many of <strong>the</strong> big problems of this century,including climate change, universal access to water,disease, and renewable energy, will require <strong>the</strong> skillsof engineers and computer scientists. When womenare not well represented in <strong>the</strong>se fields, everyonemisses out on <strong>the</strong> novel solutions that diverseparticipation brings. Moreover, a recent experimentshowed that lower-performing men are frequentlyselected over higher-performing women for ma<strong>the</strong>maticalwork (Reuben et al., 2014a). With so fewwomen working in <strong>the</strong>se fields, U.S. engineeringand technology companies are losing out on a massivetalent pool and are less globally competitivethan <strong>the</strong>y could be because <strong>the</strong>y may not be hiring<strong>the</strong> best people for <strong>the</strong> jobs.In addition, when women are so dramaticallyunderrepresented, many technical decisions arebased on <strong>the</strong> experiences, opinions, and judgmentsof only men (Williams, 2014), and needs uniqueto women may be overlooked. Schiebinger andSchraudner (2011) call for including sex and genderin all phases of research to avoid costly retrofitsand spur innovation. As Margolis and Fisher (2002,pp. 2–3) point out,Some early voice-recognition systems werecalibrated to typical male voices. As a result,women’s voices were literally unheard. …Similar cases are found in many o<strong>the</strong>rindustries. For instance, a predominantlymale group of engineers tailored <strong>the</strong> firstgeneration of automotive airbags to adultmale bodies, resulting in avoidable deaths forwomen and children.The diversity advantageUsually defined in terms of race, ethnicity, gender,disability status, age, or sexual orientation,diversity is a popular topic in <strong>the</strong> business community.Business leaders’ current attention todiversity is rooted in <strong>the</strong> civil rights laws of <strong>the</strong>1960s. In <strong>the</strong> 1980s and 1990s, some companies
AAUW 11A higher return on a college degreeEngineering and computing typically offer a higherreturn on educational investment than do o<strong>the</strong>r STEMfields. In general, engineering and computing jobs areless likely than o<strong>the</strong>r scientific jobs to require educationbeyond a bachelor’s degree. Only 1 percent of jobsin engineering and computing fields, 2 compared withjust under 30 percent of science and math jobs, 3 requiremore education than a bachelor’s degree for an entrylevelposition. This means that engineering and computingoccupations require less investment of time andmoney in education compared with many o<strong>the</strong>r STEMoccupations. At <strong>the</strong> same time, engineering and computingjobs tend to pay better than o<strong>the</strong>r STEM jobs. Infact, nearly all of <strong>the</strong> highest-paid STEM workers—94percent of workers in <strong>the</strong> 25 highest-paying STEM occupations—areengineers or computing professionals(AAUW analysis of U.S. Department of Labor, Bureau ofLabor Statistics, 2014e).Job opportunities are also more numerous in engineeringand computing than in o<strong>the</strong>r STEM fields, numberingapproximately 5.5 million jobs in 2013. Because engineeringand computing jobs make up 80 percent of <strong>the</strong>STEM labor market (Landivar, 2013) and computing jobsin particular are predicted to grow dramatically over <strong>the</strong>next decade, many more engineering and computingjobs than o<strong>the</strong>r types of STEM jobs are expected to beavailable in <strong>the</strong> near future.began promoting tolerance and multiculturalismin <strong>the</strong> workplace, having recognized <strong>the</strong> need toadvance working relationships among co-workersof diverse backgrounds (Anand & Winters, 2008).More recently, discussions about diversity focus onenhancing business performance (Catalyst, 2013).Diversity is widely linked to positive outcomes,such as greater innovation and productivity. Acommon <strong>the</strong>me among researchers boils down to<strong>the</strong> advantages of combining ideas among individualsin a group. Indeed, scholars have found thatdiverse people working toge<strong>the</strong>r can outperform<strong>the</strong> “lone genius with a high IQ” (Page, 2007). Onestudy found that gender diversity contributed to<strong>the</strong> “collective intelligence” of <strong>the</strong> group (Woolleyet al., 2010). The intellectual process of resolvingdifferences itself is seen as important. Thoughpeople often feel more comfortable with o<strong>the</strong>rs like<strong>the</strong>mselves, homogeneity can hamper <strong>the</strong> exchangeof different ideas (Phillips et al., 2009). Studies specificallyfocusing on gender diversity have demonstrateda strong connection with corporate performance(Catalyst, 2004, 2011). Numerous studieshave connected a higher representation of womenat all levels of organizations, from board membersto employees, with better outcomes (NationalCenter for Women and Information Technology,2014b).Of course, diversity does not always resultin better outcomes. Diversity can have negativeeffects, such as decreased cooperation, reducedcohesiveness, and increased turnover, largely stemmingfrom social categorization—in o<strong>the</strong>r words,an “us-versus-<strong>the</strong>m” mentality (Roberge & vanDick, 2010). One study of women’s representationon executive boards in <strong>the</strong> United States found thatdiversity could inhibit or propel strategic change,depending on <strong>the</strong> economic circumstances of <strong>the</strong>company (Triana et al., 2013).While <strong>the</strong> benefits of diversity are not automatic,<strong>the</strong> rewards are attainable (Campbell etal., 2013). To achieve a benefit, researchers callfor a “diversity mindset,” defined as learning andbuilding from diversity (van Knippenberg et al.,2013). A diversity mindset is especially valuable forwork involving exploration, creativity, and complexthinking. By adopting a diversity mindset, organizationalleaders can help cement <strong>the</strong> positive effectsof diversity and minimize <strong>the</strong> negative effects byimplementing fair employment practices and integratingexcluded groups into <strong>the</strong> workplace culture(Nishii, 2013).
12SOLVING THE EQUATIONHigh-quality jobsIn addition to encouraging innovation, increasingwomen’s representation in engineering and computingalso promotes gender equity. Both fieldsoffer good salaries. Especially among workerswithout graduate and professional degrees, earningsin engineering and computing outpace o<strong>the</strong>r fields(Carnevale et al., 2011). The average starting salaryfor individuals with a bachelor’s degree in engineeringor computing was approximately $62,000in 2014, tens of thousands of dollars higherthan annual salaries among o<strong>the</strong>r recent collegegraduates (National Association of Colleges andEmployers, 2014; AAUW, 2012). When womenaren’t well represented in <strong>the</strong>se fields, womenand <strong>the</strong>ir families miss out on <strong>the</strong> good salariesthat engineering and computing occupations canprovide.Workplace flexibilitySome evidence shows that engineering and computingjobs tend to offer more-flexible hours andwork locations than many o<strong>the</strong>r jobs. A recentanalysis found that engineering, technology, andscience occupations offer greater time flexibility andmore independence in determining tasks, priorities,and goals compared with business, health, andlaw professions (Goldin, 2014). Ano<strong>the</strong>r analysisfound that women in STEM fields, predominantlyinformation technology and engineering, workedslightly fewer hours than did women in o<strong>the</strong>rprofessional occupations, such as management,financial operations, and nursing, and were morelikely to have flexible schedules than those o<strong>the</strong>rprofessionals had (Glass et al., 2013).Ano<strong>the</strong>r indicator of flexibility is <strong>the</strong> optionto work from home. Computer and engineeringworkers are increasingly likely—and more likelythan workers in many o<strong>the</strong>r fields—to work fromhome. The U.S. Census Bureau (Mateyka et al.,2012) <strong>report</strong>s that in 2010, more than 400,000computing, engineering, and science workers usuallyworked from home, a 69 percent increase since2000—and a faster rate of growth than seen in anyo<strong>the</strong>r occupational field. The only workers whowere more likely to work from home were those inmanagement, business, and financial fields. In additionto workers who usually work from home, manymore work from home some of <strong>the</strong> time. 4Job satisfactionMany people appear to find engineering and computingjobs to be very satisfying. In a recent analysisof 25,000 volunteer respondents, CareerBliss(2014), a job listings website focused on helpingworkers find “happiness in <strong>the</strong> workplace,” <strong>report</strong>edthat <strong>the</strong> “happiest job” of 2014 (out of 169 jobs)was database administrator and <strong>the</strong> second happiestjob was quality assurance engineer. All told, thissurvey found that four of <strong>the</strong> top 10 happiest jobswere engineering or computing jobs.The gender pay gapFinally, while women in engineering and computing,like women in virtually all occupations, are paidless than men, <strong>the</strong> gender gap in earnings tends tobe substantially narrower in engineering and computingoccupations than in <strong>the</strong> overall labor force.In <strong>the</strong> overall population of full-time workers, atypical woman is paid 78 cents for every dollar paidto a typical man (U.S. Census Bureau, 2014b). Infields such as mechanical engineering and computerprogramming, women are paid more than90 cents for every dollar paid to men for full-timework. In engineering and many o<strong>the</strong>r occupations,this gap is largely associated with seniority, withlittle gap among early-career engineers, a slightgap for mid-career, and a larger gap for late-careerengineers (Frehill, 2011; AAUW, 2014). See figureA3 in <strong>the</strong> appendix for more information.EngineeringEngineers design, build, and test products.Merriam-Webster defines engineering as “<strong>the</strong>application of science and ma<strong>the</strong>matics by which<strong>the</strong> properties of matter and <strong>the</strong> sources of energyin nature are made useful to people.” Branchesof engineering include aerospace, biomedical,chemical, civil, electrical, environmental, industrial,mechanical, and systems, and within <strong>the</strong>sebranches, subdisciplines exist. 5
AAUW 13As Vassar, Bryn Mawr, and o<strong>the</strong>r women’s collegesbegan teaching physics, biology, and chemistryin <strong>the</strong> late 1800s, <strong>the</strong> idea of an engineeringdepartment for female students was never considered.A handful of women earned U.S. engineeringdegrees in <strong>the</strong> late 1800s and early 1900s, but <strong>the</strong>yremained outliers (Bix, 2014). By <strong>the</strong> 1950s womenmade up fewer than 1 percent of engineeringstudents in colleges and universities. Changing thissituation required all-male engineering programsto admit women, which institutions began to doduring World War II and through <strong>the</strong> 1960s. Evenafter women were officially allowed into engineeringprograms, however, historic ties to male-dominatedapprenticeships and powerful cultural normsmarked engineering as “male.” As Bix argues,“Women’s engineering ambitions were of a moredeeply transgressive nature (than women’s scientificambitions) because technical knowledge—withits ties to industry, heavy manual labor, and <strong>the</strong>military—was a far more masculine domain thanscience.” As an indication of just how masculine<strong>the</strong> field of engineering has been, one review foundthat in 1920, “<strong>the</strong> U.S. Census Bureau <strong>report</strong>ed that<strong>the</strong> 41 women who were enumerated as engineerswere most likely mistaken about <strong>the</strong>ir jobs” (Frehill,2004, p. 384).While men make up <strong>the</strong> majority of all typesof engineers, some engineering fields are moremale-dominated than o<strong>the</strong>rs. For example, womenmake up just 6 to 8 percent of petroleum, mechanical,and electrical and electronics engineers. Inindustrial, biomedical, and environmental engineering,on <strong>the</strong> o<strong>the</strong>r hand, women make up 17 to 21percent of working engineers (see figure 4).Engineering for boys, home economics for girlsHistorically, many engineering-minded women wereencouraged to study home economics ra<strong>the</strong>r than engineering(Bix, 2002). Ellen Swallow Richards, <strong>the</strong> founderof <strong>the</strong> American Home Economics Association (as well asa co-founder of <strong>the</strong> Association of Collegiate Alumnae,<strong>the</strong> predecessor organization to AAUW), was trained asa chemist at Vassar and MIT (Stage, 1997) and conductedimportant water-quality research that led to <strong>the</strong> firstmodern U.S. municipal sewage treatment plant.Describing <strong>the</strong> history of <strong>the</strong> development of <strong>the</strong> homeeconomics discipline, Alice Pawley (2012, p. 63) wrote,The founders of home economics wantedwomen to apply <strong>the</strong> logic of scientific managementto domestic contexts to develop better,more effective, and more efficient ways ofoperating <strong>the</strong> home. Improved health, hygiene,and sanitation, improved knowledge of nutrition,more efficient technologies for lighting,heating, and cleaning, and management techniquesfor supervising servants and raisingchildren, all organized around <strong>the</strong> home, constituted<strong>the</strong> realm of a new, science-orientedunderstanding of <strong>the</strong> domestic sphere, createdand maintained by women. What is crucialabout this history with respect to <strong>the</strong> constructionof engineering is <strong>the</strong> realization that<strong>the</strong> actual tasks awarded to home economicscould easily have been considered “science” or“engineering” tasks had <strong>the</strong>y been in a differentcontext.According to one analysis of <strong>the</strong> study of home economicsat Iowa State University, engineering majorsand home economics majors learned very similar conceptsbut related <strong>the</strong>m to different applications (Bix,2002). Whereas agricultural engineering majors tookapart and inspected tractors, home economics majorsdisassembled and evaluated stoves. While mechanicalengineering majors learned <strong>the</strong> <strong>the</strong>rmodynamics behinddiesel engines, home economics majors learned about<strong>the</strong> physics of refrigeration. This historical context ofintentional segregation of women and men into differentfields, despite <strong>the</strong> similar technical underpinnings of<strong>the</strong>ir work, helps explain why progress in increasing <strong>the</strong>representation of women in engineering has been slow.
14SOLVING THE EQUATIONFIGURE 4. WOMEN IN ENGINEERING, COMPUTING, AND SELECTED OTHER OCCUPATIONS, 2013Petroleum6%Electrical and electronics8%Mechanical8%Aerospace9%OTHER PROFESSIONALS COMPUTING PROFESSIONALS ENGINEERSComputer hardwareCivilMining and geological,including mining safetyEngineers, all o<strong>the</strong>rChemicalMaterialsIndustrial, includinghealth and safetyBiomedicalEnvironmentalComputer network architectsNetwork and computersystems administratorsInformation security analystsSoftware developers,applications and systemsComputer and informationresearch scientistsComputer occupations, all o<strong>the</strong>rComputer programmersComputer support specialistsDatabase administratorsComputer systems analystsWeb developersLawyersPhysicians and surgeonsChemists and materials scientistsBiological scientistsSecondary school teachersMedical scientists11%11%13%13%14%16%17%20%21%7%18%19%20%20%23%24%28%32%Overall U.S. full-time workforce: 44% women36%39%35%36%39%50%55%56%Registered nurses0 20 40 60 80 100Percentage of full-time professionals who are womenNotes: Occupations are self-<strong>report</strong>ed. All occupations designated as computer and engineering occupations by <strong>the</strong> U.S. Department of Labor, Bureau of Labor Statistics, thatemployed at least 500 men and 500 women in 2013 are shown. Occupations shown in “o<strong>the</strong>r professionals” are selected professions shown for reference.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014c).89%
AAUW 15ComputingLike engineering, computing is all around us,influencing how we work, play, and communicate.Computing professionals work with computersystems, including hardware, software, and networkingtechnology. They design and producehardware components such as computer chips andhard drives, design computer systems and networks,develop and test software, perform maintenance ontechnology, provide support to users of informationtechnology products, and design video games. 6Computing professionals are employed not onlyby technology companies but across nearly everysector of <strong>the</strong> workforce. In <strong>the</strong> modern world, inwhich society depends on technology and computers,most workplaces employ computing professionalsin some form.Women’s representation in computing has takena different path than women’s representation inany o<strong>the</strong>r field. While today fewer than one in fivecomputer science degrees is awarded to a woman,and women make up around a quarter of <strong>the</strong>computing workforce, women were not always sosparsely represented in computing.Women were a significant presence in <strong>the</strong> earlydecades of computing. Ada Lovelace, considered<strong>the</strong> first computer programmer, was an early pioneerwho lived in <strong>the</strong> mid-1800s. Women made up<strong>the</strong> majority of programmers during World WarII (Abbate, 2012). Kay McNulty, Betty Snyder,Marlyn Wescoff, Ruth Lichterman, Jean Jennings,and Frances Bilas were <strong>the</strong> first programmers of <strong>the</strong>first programmable electronic computer, ENIAC(Electronic Numerical Integrator and Computer).Graduates of a University of Pennsylvania course totrain women to perform ballistics calculations, <strong>the</strong>women were originally hired as “subprofessional”workers but were later promoted to professionalstatus as a result of <strong>the</strong>ir innovative work in developingprogramming procedures for <strong>the</strong> ENIAC,which consisted of 18,000 vacuum tubes (Abbate,2012; Light, 1999). Rear Admiral Grace Hopper,ano<strong>the</strong>r early pioneer, created <strong>the</strong> first compiler in1952.With a few notable exceptions, however, womenworking in computing in <strong>the</strong> mid-1900s wereviewed as low-status clerical employees. Computeroperators inherited <strong>the</strong>ir positions and status from<strong>the</strong> clerical, administrative, data-processing work ofkey punch operators, as well as from women whohad been employed as “human computers” to docalculations by hand (Misa, 2010; Abbate, 2012;Schlombs, 2010). Koput and Gutek (2010, p. 92)describe women moving into <strong>the</strong> computer andinformation technology sector, often in “basementjobs” in “<strong>the</strong> bowels of large corporations.” Thesejobs were not part of any career ladders; <strong>the</strong>refore,workers were not well compe<strong>nsa</strong>ted and were usuallyoverlooked for career advancement.Throughout <strong>the</strong> mid-1900s, computing wasstill a new field that lacked a gender identity andneeded workers, and it attracted women as well asmen (Ensmenger, 2010b; Koput & Gutek, 2010;Abbate, 2012). Yet, as Koput and Gutek (2010, p.103) wrote, “Over a relatively short period of time,a field that was once relatively gender integratedhas become solidly male dominated.” Computinghistorians and researchers have proposed a numberof explanations, including hiring practices in <strong>the</strong>1960s and 1970s that favored men, <strong>the</strong> increasingprofessionalization of <strong>the</strong> field, a stronger connectionwith <strong>the</strong> technical engineering culture, amale gaming culture that entered computing with<strong>the</strong> rise of <strong>the</strong> personal computer, and increasinglystringent requirements for being admitted toan undergraduate computing program due to <strong>the</strong>increasing popularity of <strong>the</strong> field.Ensmenger (2010b) found that companiesin <strong>the</strong> 1960s and 1970s looking for potentialcomputer programmers often used aptitude andpersonality tests that privileged male-stereotypedcharacteristics. He argued that <strong>the</strong> use of personalitytests led to a feedback cycle in which companieshired “antisocial, ma<strong>the</strong>matically inclined males,”perpetuating <strong>the</strong> belief that programmers should beantisocial, ma<strong>the</strong>matically inclined males (p. 78).Ensmenger (2010a) also points to efforts tobring prestige and structure to <strong>the</strong> field of computing,which involved <strong>the</strong> creation of professionalorganizations, networks, and hierarchiesthat encouraged and facilitated <strong>the</strong> entry of men.Abbate (2012) argues that this effort to professionalize<strong>the</strong> field fur<strong>the</strong>r connected computing with
16SOLVING THE EQUATIONengineering, specifically engineering’s technical andanalytical focus and its prestige as a professionalfield. On university campuses an increasing proportionof computer science programs became locatedwithin engineering colleges (Misa, 2010). Thepercentage of women earning bachelor’s degreesin computer science has been shown to be lower atschools where computing programs are located inengineering colleges compared with schools thatlocate <strong>the</strong>ir computing departments in colleges ofarts and sciences (Camp, 1997).But what happened specifically in <strong>the</strong> 1980sthat caused women’s representation in computingto drop? One argument points to <strong>the</strong> rise of<strong>the</strong> personal computer. IBM launched <strong>the</strong> personalcomputer in 1981, and Apple introduced<strong>the</strong> Macintosh in 1984. Before that, few people’shomes or businesses had computers, and girls andboys had similar exposure to computers—generallynone. Once computers were in <strong>the</strong> home, <strong>the</strong>ywere rapidly adopted by men and boys as a newkind of toy (Margolis & Fisher, 2002). Haddon(1992), who studied <strong>the</strong> origins of <strong>the</strong> home computermarket in England, argued that <strong>the</strong> personalcomputer linked <strong>the</strong> computing industry to <strong>the</strong>electronic “hobbyist” culture and earlier game-playingarenas, such as arcades, that were primarily <strong>the</strong>domain of men and boys. The gamer culture thathas since developed can be particularly inaccessiblefor women (Parkin, 2014).Some have suggested that women beganearning a declining proportion of computer sciencebachelor’s degrees in <strong>the</strong> 1980s because ofa surge in interest in computing as an academicdiscipline. This boom overloaded <strong>the</strong> capacityof academic computer science departments,and schools responded by imposing increasinglystringent requirements for entry and completion of<strong>the</strong> major. These requirements disproportionatelydisadvantaged women and people of o<strong>the</strong>r underrepresentedgroups who were generally enteringcollege with less programming experience and lessmath preparation (Roberts, 1999, 2011).As in engineering, <strong>the</strong> proportion of womenacross computing disciplines varies significantly,with women making up about 39 percent of webdevelopers but only 7 percent of computer networkarchitects. In no engineering or computing occupationdoes women’s representation match that in <strong>the</strong>overall full-time labor force (44 percent), and comparedwith professional occupations overall, wherewomen make up 57 percent of <strong>the</strong> workforce (U.S.Department of Labor, Bureau of Labor Statistics,2014a, table 9), women are dramatically underrepresentedin computing (and engineering) jobs.Women are best represented among web developers(39 percent), computer systems analysts (36 percent),and database administrators (32 percent). Inall o<strong>the</strong>r computing (and all engineering) occupations,women account for less than 30 percent ofworkers, far below parity (see figure 4).Preparing K–12 studentsfor engineering andcomputingMath and science courses in elementary, middle,and high school can prepare students for pursuingengineering or computing majors in collegeand, especially for engineering, are often required.Ma<strong>the</strong>matics can also be helpful for those whoenter <strong>the</strong>se fields with an associate degree orcertificate or by ano<strong>the</strong>r means. In elementaryand middle school, girls and boys tend to earnsimilar grades in math and science courses andto have similar scores on standardized math andscience exams (Snyder & Dillow, 2013). In highschool, girls and boys have earned approximately<strong>the</strong> same number of math and science credits, andgirls have been doing slightly better than boys in<strong>the</strong>se classes, since <strong>the</strong> 1990s (U.S. Departmentof Education, National Center for EducationStatistics, 2011). Boys are more likely, however, totake <strong>the</strong> advanced placement exams most closelyassociated with engineering and computing andtend to outscore girls on <strong>the</strong>se exams and <strong>the</strong> SATby a small margin (College Board, 2013a, 2013b).How relevant are <strong>the</strong>se small gender differencesin early science and math achievement to women’sunderrepresentation in engineering or computing?A recent study by researchers at <strong>the</strong> University ofTexas and <strong>the</strong> University of Minnesota found thatgender differences in achievement in high school,including women’s underrepresentation among
AAUW 17those earning <strong>the</strong> highest standardized math testscores, accounted for very little of <strong>the</strong> gender differencein <strong>the</strong> likelihood of choosing an engineering,computing, math, or physical science major incollege (Riegle-Crumb et al., 2012). The researchersdetermined that while some gender differences inSTEM achievement existed before college, <strong>the</strong>sedifferences did not appear to be a strong determinantof who ended up majoring in engineering andcomputing.Ra<strong>the</strong>r than achievement, interest in STEMfields in high school is more closely associated with<strong>the</strong> pursuit of an engineering or computing educationor career in adulthood (Maltese & Tai, 2011;Benbow, 2012). While achievement in an areaoften contributes to interest, beginning in youngadolescence and increasingly as girls and boys movethrough middle school and high school, boys tendto express more positive attitudes toward and interestin STEM subjects than girls do (Sandrin &Borror, 2013; Iskander, Gore et al., 2013; Diekman,Weisgram et al., 2015; Else-Quest et al., 2013;Weisgram & Bigler, 2006; Weisgram & Diekman,2014).One nationally representative study found that<strong>the</strong> key factor predicting STEM career interestat <strong>the</strong> end of high school was interest at <strong>the</strong> startof high school (Sadler et al., 2012). While it iscertainly possible to decide to pursue a career inengineering or computing well after high schoolgraduation, <strong>the</strong>se findings suggest that earlyexposure to engineering and computing that sparksan interest in <strong>the</strong>se fields is often a precursor toactually pursuing a career in <strong>the</strong>se fields. For thisreason, advocates for improving gender diversity inengineering and computing often focus interventionson increasing interest in <strong>the</strong> fields amongelementary and middle school students (Valla &Williams, 2012). One recent survey found thatsocial encouragement from family, friends, andeducators, regardless of <strong>the</strong>ir technical expertise, is<strong>the</strong> factor most likely to encourage girls’ interest incomputer science (Google, 2014b).Lack of computing education in K–12 schoolsJust 19 percent of high school graduates <strong>report</strong>ed earningat least one credit in a computing course during highschool in 2009, and fewer girls (14 percent) than boys (24percent) <strong>report</strong>ed taking such courses. These percentagesare smaller than in both 1990 and 2000, when 25percent of high school graduates <strong>report</strong>ed taking computingclasses (U.S. Department of Education, NationalCenter for Education Statistics, 2011). According to <strong>the</strong>National Center for Women and Information Technology(2014a), most students in <strong>the</strong> United States are notexposed to a rigorous computing education in highschool or earlier for several reasons:1. Only about half of <strong>the</strong> states allow computing classesto count as an academic subject toward high schoolgraduation (Code.org, 2014). Many states that doallow computing classes to count have only recentlyadopted this policy. AAUW and o<strong>the</strong>r organizationssuch as <strong>the</strong> Computer Science Teachers Associationand Code.org have worked to encourage states tocount high school computing credits as part of <strong>the</strong>math or science credits required for graduation.2. Computing courses are sometimes included in vocationalcourse choices and, <strong>the</strong>refore, may not attractcollege-bound students.3. Computing as an elective course competes witho<strong>the</strong>r attractive electives, such as music and foreignlanguages.4. Computing is often taught by individuals with ei<strong>the</strong>rlittle background in <strong>the</strong> subject or little teachingexperience.Computing classes for middle and high school studentsintroduce girls and boys to <strong>the</strong> subject at an influentialage. These courses are especially useful for sparkinggirls’ interest in computing because girls are less likelythan boys to pursue computing on <strong>the</strong>ir own outside of aformal learning environment (Margolis & Fisher, 2002).
18SOLVING THE EQUATIONFIGURE 5. INTENT OF FIRST-YEAR COLLEGE STUDENTS TO MAJOR IN STEM FIELDS, BY GENDER, 2014252019%WomenMenPercentage of students151016%11%6%6%50Biological andlife sciencesEngineering1%Chemistry1% 1%Computerscience1% 1%Ma<strong>the</strong>matics/statistics0.3%Physics1%Source: AAUW analysis of Eagan et al. (2014).Higher educationA gender gap in students’ intention to pursuebachelor’s degrees in engineering or computingis evident by <strong>the</strong> time <strong>the</strong>y step foot on collegecampuses. 7 Young men are much more likely than<strong>the</strong>ir female peers to begin college intending tomajor in engineering and computing (see figure 5).While nearly one out of every five young men whostart college intends to major in engineering, onlyone out of 17 young women starting college has<strong>the</strong> same intention. Computing is a much smalleracademic field, and 6 percent of young men whostart college intend to major in computing comparedwith just 1 percent of young women. Thisdivide by gender holds true across races/ethnicities:Women are far less likely than men within eachracial/ethnic category to begin college intendingto major in engineering or computing (NationalScience Foundation, National Center for Scienceand Engineering Statistics, 2014c).Of course, not all students who enroll in anengineering or computing major graduate withthose degrees. At <strong>the</strong> bachelor’s degree level, <strong>the</strong>national rate of retention from entry into <strong>the</strong> majorto graduation is approximately 60 percent forengineering and 40 percent for computing (Chen,X., 2013), and some evidence shows that this rate issimilar for women and men in engineering (Lord etal., 2013; Ohland et al., 2008). Still, because womenmake up such a small portion of those who initiallymajor in engineering and computing, understandingwhy women leave <strong>the</strong>se majors is important.As figure 6 shows, women’s representationamong engineering bachelor’s degree recipients hasincreased in a fairly linear fashion, growing from amere 1 percent in 1970 to 21 percent in 2000. After2000, participation dipped slightly, to 19 percentin 2013. Women’s participation in undergraduatecomputer science, in contrast, has been decliningfor decades. Before 1970, women earned between10 and 15 percent of <strong>the</strong> fewer than 1,000 bachelor’sdegrees awarded in computer science eachyear. Throughout <strong>the</strong> 1980s it appeared that womenmight reach parity in computing, with women
AAUW 19earning 37 percent of computing bachelor’s degreesin 1984 and 1985. After 1985, however, women’sparticipation in computing reversed course, so thatby 2013 <strong>the</strong> proportion of computing bachelor’sdegrees awarded to women was half what it hadbeen nearly three decades earlier. 8Women’s representation among engineering andcomputing graduates is much smaller than amongbachelor’s degree recipients overall, where womenreached parity with men in 1985 and earned 57percent of <strong>the</strong> degrees awarded in 2013. Likewise,in <strong>the</strong> combined total number of science and engineeringbachelor’s degrees awarded, including <strong>the</strong>social sciences and psychology, women reached paritywith men in 2000 and continued to earn half ofthose degrees awarded through 2013 (see figure 6).Among associate degree earners, women’srepresentation in engineering has hovered around14 percent since 1990. In computing, however,<strong>the</strong> representation of women has been declining.Women earned half of <strong>the</strong> associate degreesawarded in computing in 1990, but by 2013 thatnumber had dropped to 21 percent (see figure A4in <strong>the</strong> appendix).Variation in women’srepresentationThe proportion of women earning engineeringbachelor’s degrees varies substantially among engineeringdisciplines (see figure 7). Mechanical, civil,electrical, and chemical engineering are <strong>the</strong> largestengineering fields, accounting for nearly two-thirdsof engineering degrees awarded each year. Womenaccounted for 32 percent of chemical engineeringgraduates, 21 percent of civil engineering graduates,and only 12 percent of electrical and mechanicalengineering graduates in 2013. Because mechanicaland electrical engineering are such large engineeringdisciplines, women’s dramatic underrepresentationin <strong>the</strong>se fields is a big factor in women’s underrepresentationin engineering overall. Women fromunderrepresented minority (URM) groups, definedhere as engineers who identify as black, AmericanIndian, Alaska Native, or Hispanic, are also severelyunderrepresented in <strong>the</strong>se disciplines, making upjust 2 percent of those awarded bachelor’s degreesin mechanical and electrical engineering in 2013(AAUW analysis of National Science Foundation,FIGURE 6. BACHELOR’S DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–2013Percentage605040302010057%57%58%54%55%57%49%51%47%50%50% 50% 50%43%45%43%37%39%33%30% 37%30%28%29%28%22%21%19%18% 19%17%15%15%13%20%10%18% 18%1%2%1970 1975 1980 1985 1990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; ma<strong>the</strong>matics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013), and National Science Foundation, National Center forScience and Engineering Statistics (2014a).
20SOLVING THE EQUATIONFIGURE 7. BACHELOR’S DEGREES AWARDED IN ENGINEERING, BY DISCIPLINE AND GENDER, 201325,00012%22,208xx%WomenMenPercentageof women20,000Number of degrees awarded15,00010,00021% 12%32%13,509 13,0917,6785, 00039% 10% 28% 23%4,838 4,631 4,505 4,47814%3,49022%2,16830% 45% 14% 31% 25% 20% 15% 8%1,262 1,199 1,130 954 767 735 593 5760MechanicalCivilElectricalChemicalBiomedicalComputer, generalIndustrial and engineeringmanagementAll o<strong>the</strong>rAerospaceGeneralMaterialsEnvironmentalPetroleumAgriculturalArchitecturalSystemsNuclearComputer softwareSource: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).National Center for Science and EngineeringStatistics, 2014a, 2014b).The engineering fields with <strong>the</strong> largest representationof women, on <strong>the</strong> o<strong>the</strong>r hand, tend to bemuch smaller fields (see figure 7). Environmentalengineering, for example, boasts <strong>the</strong> highest representationof women, who earned 45 percent ofbachelor’s degrees in 2013; yet only about 1,200environmental engineering bachelor’s degrees wereawarded that year. 9 Environmental engineering wasalso <strong>the</strong> field with <strong>the</strong> highest percentage of URMwomen, who earned 7 percent of bachelor’s degreesawarded in 2013 (AAUW analysis of NationalScience Foundation, National Center for Scienceand Engineering Statistics, 2014a, 2014b). The fieldof bioengineering/biomedical engineering similarlyhas attracted a relatively high proportion of women(39 percent in 2013) and is somewhat larger, withapproximately 4,800 bachelor’s degrees awarded in2013.
AAUW 21FIGURE 8. BACHELOR’S DEGREES AWARDED IN COMPUTING, BY GENDER AND DISCIPLINE, 201320,00018%18,610WomenMenxx% Percentageof women15,000Number of degrees awarded10,00013%11,13517%6,565 22%5,8295,00034%3,8740Computer and informationsciences, generalComputer scienceComputer/IT administrationand managementInformation science studiesSoftware and mediaapplications10%1,828Systems networking andtelecommunications13%1,692Programming21%1,516Systems analysis18%537Computer/IS support servicesincluding data processingSource: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).Percentages aside, <strong>the</strong> largest numbers ofbachelor’s degrees awarded to women in 2013 werein civil engineering (2,894), mechanical engineering(2,676), and chemical engineering (2,478).Similarly, <strong>the</strong> largest numbers of bachelor’s degreesearned by URM women were in civil engineering(526), mechanical engineering (415), and chemicalengineering (355). At <strong>the</strong> o<strong>the</strong>r end of <strong>the</strong> spectrum,only 16 URM women were awarded bachelor’sdegrees in petroleum engineering in 2013,accounting for just 1 percent of <strong>the</strong> petroleumengineering bachelor’s degrees awarded that year(AAUW analysis of National Science Foundation,National Center for Science and EngineeringStatistics, 2014a, 2014b).The proportion of women earning computingbachelor’s degrees also varies by discipline (seefigure 8). General computer and information sciencesis <strong>the</strong> largest discipline, making up more thana third of all bachelor’s degrees awarded in 2013.Eighteen percent were awarded to women, and 4percent were awarded to URM women. Womenwere best represented in software and mediaapplications, where <strong>the</strong>y earned about a third of <strong>the</strong>
22SOLVING THE EQUATIONbachelor’s degrees awarded in 2013. Informationscience and systems analysis are <strong>the</strong> fields with <strong>the</strong>next highest percentage of women, at 22 and 21percent. These fields have <strong>the</strong> best representationof URM women as well, with URM women mosthighly represented in software and media applications(9 percent) and in information science andsystems analysis (7 percent). At <strong>the</strong> o<strong>the</strong>r end of<strong>the</strong> spectrum, women were least well representedin systems networking and telecommunications,where <strong>the</strong>y were awarded just one in 10 bachelor’sdegrees in 2013. URM women were least well representedin computer science, where <strong>the</strong>y receivedonly 2 percent of bachelor’s degrees in 2013(AAUW analysis of National Science Foundation,National Center for Science and EngineeringStatistics, 2014a, 2014b).Diversity among U.S. engineeringand computing graduatesEngineering and computing have not achieved levelsof gender and racial/ethnic diversity on par withthose of <strong>the</strong> general population between <strong>the</strong> ages20 and 24. Women of all races/ethnicities exceptAsian are underrepresented among engineering andcomputing bachelor’s degree recipients comparedwith <strong>the</strong>ir representation in <strong>the</strong> general population.White women were awarded 13 percent of <strong>the</strong>engineering and 10 percent of <strong>the</strong> computing bachelor’sdegrees in 2013, while making up 28 percentof <strong>the</strong> general population. The largest discrepancyis among URM women, who were awarded just3 percent of <strong>the</strong> engineering and 6 percent of <strong>the</strong>computing bachelor’s degrees in 2013, while makingup 18 percent of <strong>the</strong> general population (seefigure 9).How men are faringWhen it comes to men, <strong>the</strong> story is different—atleast in <strong>the</strong> number of degrees awarded. Men of allracial/ethnic groups are much better representedamong engineering and computing bachelor’sdegree earners than are women of <strong>the</strong> same group.In most cases, men are earning engineering andcomputing degrees in proportion to or better than<strong>the</strong>ir representation in <strong>the</strong> general population (seefigure 9 and figure A5 in <strong>the</strong> appendix). This istrue for white and Asian American men in bo<strong>the</strong>ngineering and computing. Black men, too, whorepresented 8 percent of <strong>the</strong> 20- to 24-year-olds in<strong>the</strong> United States, earned 9 percent of computerscience bachelor’s degrees in 2013. 10 AmericanIndian/Alaska Native men, who represented just0.5 percent of 20- to 24-year-olds in <strong>the</strong> UnitedStates, earned 0.5 percent of <strong>the</strong> computer sciencebachelor’s degrees in 2013 (see figure A5 in <strong>the</strong>appendix).Toge<strong>the</strong>r, black, Hispanic, and AmericanIndian/Alaska Native men were awarded 18 percentof computing bachelor’s degrees in 2013 andmade up 19 percent of <strong>the</strong> general population of20- to 24-year-olds. This near parity is especiallynoteworthy given that men from <strong>the</strong>se racial/ethnicgroups are significantly underrepresented amongbachelor’s degree earners overall, making up just 9percent of those earning bachelor’s degrees in anyfield in 2013. Any underrepresentation of black,Hispanic, and American Indian/Alaska Nativemen among engineering and computing bachelor’sdegree holders is largely symptomatic of <strong>the</strong> issueof insufficient diversity in higher education overall(Su, 2010). In <strong>the</strong> engineering and computingworkforce, however, black, Hispanic, and AmericanIndian/Alaska Native men are underrepresented.Between 2006 and 2010, black men made up just 4percent of <strong>the</strong> engineering and computing workforce.Hispanic men made up 5 percent of <strong>the</strong> engineeringand 4 percent of <strong>the</strong> computing workforce,and American Indian/Alaska Native men madeup 0.2 percent of <strong>the</strong> engineering and computingworkforce.Diversity of those with advanced degreesFigure 10 underscores <strong>the</strong> degree to which graduatesof engineering and computing programs arenot representative of <strong>the</strong> U.S. population andprovides a look at <strong>the</strong> breakdown at <strong>the</strong> master’sand doctoral levels as well. 11 While white womenmade up 30 percent of high school graduates in2012 and URM women made up 18 percent, just7 percent of doctoral degrees in engineering andcomputing were awarded to white women, andjust 2 percent were awarded to URM women.
AAUW 23FIGURE 9. POPULATION AGES 20–24 AND BACHELOR'S DEGREES AWARDED IN SELECTED FIELDS,BY RACE/ETHNICITY AND GENDER, 2013Population, 20–24 Years Old(n = 22,252,501)3%All Bachelor’s Degrees(n = 1,658,333)4%29%18%30%15%19%28%9%3%39%3%Computing Bachelor’s Degrees(n = 44,193)51%Engineering Bachelor’s Degrees(n = 76,246)2%6%3% 3%10%13%8%10%56%59%18%12%URM womenWhite womenAsian American women Asian American menURM menWhite menNotes: Charts include only U.S. citizens and permanent residents. U.S. citizens and permanent residents of “o<strong>the</strong>r/unknown races/ethnicities” (which includes students who <strong>report</strong>multiple race/ethnicities) and temporary residents are not included. Computing included 5,276 o<strong>the</strong>r and 2,365 temporary residents, and engineering included 4,637 o<strong>the</strong>r and6,331 temporary residents who earned bachelor’s degrees and, <strong>the</strong>refore, were not included in <strong>the</strong>se data. Underrepresented minority (URM) includes American Indians and AlaskaNatives, blacks, and Hispanics/Latinos.Sources: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b), and U.S Census Bureau (2014d).
24SOLVING THE EQUATIONFIGURE 10. STUDENT POPULATION FROM HIGH SCHOOL TO DOCTORATE IN ENGINEERING ORCOMPUTING, BY RACE/ETHNICITY AND GENDER, 20121008018%30%3%18%31%3%4%11%8%12%2%7%6%6%30%3%3%7%5%24%2%2%3%Percentage604016%3%14%3%55%12%12%46%30%28%33%200High schoolgraduates(n = 3,014,670)1%2%First-time, full-timefreshmen incollege overall(n = 2,268,993)6%Bachelor’s degreein engineeringor computing(n = 121,310)1%Master’s degreein engineeringor computing(n = 59,952)Ph.D. in engineeringor computing(n = 10,084)Asian womenAsian menURM womenURM menWhite women Temporary resident womenWhite men Temporary resident menNotes: Students and degree recipients <strong>report</strong>ed as “o<strong>the</strong>r/unknown races/ethnicities” were excluded from <strong>the</strong> totals and percentages <strong>report</strong>ed here. As a result, <strong>the</strong> following numbersare excluded: first-time, full-time freshmen, 185,677; bachelor’s degree, 9,913; master’s degree, 4,199; and Ph.D., 479. Underrepresented minority (URM) includes AmericanIndians and Alaska Natives, blacks, and Hispanics/Latinos.Sources: Prescott & Bransberger (2012); L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b).Part of <strong>the</strong> reason for <strong>the</strong> small percentages is thatmore than half (58 percent) of engineering andcomputing doctorates were awarded to temporaryresidents, four of five of whom were men. Lookingonly at U.S. citizens and permanent residents, 16percent of engineering and computing doctorateswere awarded to white women, and 4 percentwere awarded to URM women (AAUW analysisof National Science Foundation, National Centerfor Science and Engineering Statistics, 2014b), stillwell below <strong>the</strong> representation of white and URMwomen in <strong>the</strong> overall population. In sum, engineeringand computing largely remain men’s domains at<strong>the</strong> college level, and URM women are particularlyunderrepresented (Ong, 2011).People with disabilitiesIncreasing <strong>the</strong> number of people with disabilitiesin engineering and computing is ano<strong>the</strong>r part ofbuilding diversity. About 12 percent of <strong>the</strong> populationhas some kind of disability (National ScienceFoundation, National Center for Science andEngineering Statistics, 2014c). College studentswith disabilities are somewhat more likely tomajor in a STEM field than are students withoutdisabilities (Lee, 2011). Students with autismspectrum disorder are especially likely to select amajor in a STEM field (Wei et al., 2014). Studentswith disabilities are about as likely to graduate inengineering or computing as are students withoutdisabilities; however, people with disabilities are
AAUW 25underrepresented in <strong>the</strong> engineering and computingworkforce relative to <strong>the</strong> workforce as a whole.An estimated 6 percent of working computingprofessionals and 7 percent of working engineers<strong>report</strong> having a disability, compared with 9 percentof workers overall (National Science Foundation,National Center for Science and EngineeringStatistics, 2014c).Training for <strong>the</strong> engineeringworkforceMost working engineers (80 percent) have at leasta bachelor’s degree, with just over a quarter holdingan advanced degree (23 percent hold a master’sdegree, and 5 percent hold a doctorate). Mostengineers with less than a bachelor’s degree levelof education have an associate degree (AAUWanalysis of U.S. Census Bureau, 2011b).The percentage of women among engineeringgraduates varies dramatically among institutions.Many different kinds of institutions have succeededin graduating a relatively large proportionof women in <strong>the</strong>ir engineering programs. The fewwomen’s colleges that have engineering programs,such as Smith College and Sweet Briar College,graduate 100 percent women. Historically blackcolleges and universities (HBCUs), such as PrairieView A&M University, which awarded 66 percentof its engineering bachelor’s degrees to women in2012, and top-rated schools, such as MassachusettsInstitute of Technology and California Institute ofTechnology, both of which awarded 44 percent of<strong>the</strong>ir bachelor’s degrees in engineering to women in2012, have also succeeded in graduating engineeringclasses with a higher than average percentage ofwomen. At most of <strong>the</strong> largest engineering schoolsin <strong>the</strong> country, women make up about one of everyfive graduates (see figures A6a and A6b in <strong>the</strong>appendix).Training for <strong>the</strong> computingworkforceComputing professionals arrive at <strong>the</strong>ir occupationsby more varied means than engineers do.Computing jobs are available for workers at all levelsof educational achievement. A small percentage(6 percent) of computing professionals hold only ahigh school diploma or GED (general educationaldevelopment), and 1 percent have not graduatedfrom high school. About a third (29 percent) ofcomputing professionals have some college experience,a certificate (typically requiring one year ofstudy), or an associate degree (typically requiringtwo years of study). 12 Most computing professionals(about two-thirds) have a bachelor’s degree orhigher; 44 percent hold a bachelor’s degree, 18percent hold a master’s degree, and 2 percent hold adoctorate (AAUW analysis of U.S. Census Bureau,2011b).Of computing professionals with bachelor’sdegrees or higher, only about half have bachelor’sdegrees in computing, engineering, or ma<strong>the</strong>maticalsciences. 13 Nearly a fifth of computing professionalswith bachelor’s degrees were businessEducational attainment of<strong>the</strong> engineering andcomputing workforceIn this <strong>report</strong> AAUW follows <strong>the</strong> protocol of <strong>the</strong> U.S.Department of Labor, Bureau of Labor Statistics,and <strong>the</strong> U.S. Census Bureau in defining “engineers”and “computer occupations.” Both <strong>report</strong> workforceinformation separately for “engineers” and “drafters,engineering technicians, and mapping technicians,”effectively providing data separately forengineering jobs that require bachelor’s degreesand those that do not.In contrast <strong>the</strong> category of “computer occupations”combines occupations that require varying levelsof education, including those that require a doctorateand those that require an associate degreeor less (U.S. Department of Labor, Bureau of LaborStatistics, 2009). So, for example, while AAUW doesnot include engineering technicians and drafters in<strong>the</strong> definition of engineers, computer support personnelare included in <strong>the</strong> definition of computingworkers. The result is that <strong>the</strong> educational backgroundsof <strong>the</strong> engineers and computing professionalsthat are <strong>the</strong> subject of this <strong>report</strong> are somewhatdifferent.
26SOLVING THE EQUATIONmajors, and o<strong>the</strong>rs majored in <strong>the</strong> social sciences,<strong>the</strong> physical or biological sciences, communications,and psychology (AAUW analysis of U.S. CensusBureau, 2014a). 14The approximately one-third of computer workerswho hold technical bachelor’s degrees attendedmany different kinds of institutions, includingpublic, private nonprofit, and private for-profitinstitutions. The University of Phoenix, a for-profitinstitution, was far and away <strong>the</strong> largest institutionin <strong>the</strong> United States to grant bachelor’s degrees incomputing in 2012, awarding just over 3,000. As inengineering, <strong>the</strong> percentage of computing degreesawarded to women ranges widely from less than10 percent at some institutions to two-thirds ofdegrees at o<strong>the</strong>r institutions, and many differentkinds of institutions have succeeded in graduatinga large proportion of women in computingprograms. For-profit schools, including several ArtInstitute campuses; HBCUs, such as Johnson C.Smith University; 15 and public and private institutions,such as Northwest Missouri State University,North Carolina Wesleyan College, and HarveyMudd College, are among <strong>the</strong> top institutions inwomen’s representation among computing graduates(see figures A6c and A6d in <strong>the</strong> appendix).The engineering andcomputing workforceThe great demand for workers in <strong>the</strong> engineeringand computing fields is an important reasonto attract more women (Sevo, 2009). Overall,employment in <strong>the</strong> United States is projected togrow by approximately 15 million jobs (11 percent)between 2012 and 2022. The U.S. Department ofLabor, Bureau of Labor Statistics (2014d), predictsthat computing occupations will grow at asubstantially faster rate of 18 percent. Some of <strong>the</strong>fastest-growing computing occupations are amongthose expected to produce <strong>the</strong> most growth in rawnumbers of jobs as well, resulting in 1.2 millioncomputing job openings between 2012 and 2022.Computing occupations that are predicted to growespecially quickly (see figure A7 in <strong>the</strong> appendix)include <strong>the</strong> relatively small field of informationsecurity analysts (projected to grow 37 percent by2022); computer systems analysts (25 percent);and <strong>the</strong> largest computing occupation and largestSTEM occupation overall (Landivar, 2013),applications software developers (23 percent). Allof <strong>the</strong>se occupations require a bachelor’s degree foran entry-level position (U.S. Department of Labor,Bureau of Labor Statistics, 2014e), and most workersin <strong>the</strong>se occupations have a bachelor’s degree orhigher in computing or engineering fields (AAUWanalysis of National Science Board, 2014, appendixtable 3-3).Engineering occupations, on <strong>the</strong> o<strong>the</strong>r hand,are predicted to grow at a slower rate of 9 percent,resulting in slightly more than 500,000 engineeringjob openings between 2012 and 2022. Theengineering occupations predicted to grow at <strong>the</strong>fastest rates (see figure A8 in <strong>the</strong> appendix) include<strong>the</strong> relatively small field of biomedical engineering(predicted to grow by 27 percent by 2022), <strong>the</strong>similarly small field of petroleum engineering (26percent), and one of <strong>the</strong> largest engineering fields,civil engineering (20 percent).Retention in engineeringIn addition to being less likely than men to gointo engineering in <strong>the</strong> first place, women appearto be less likely than men to stay in <strong>the</strong> engineeringprofession (Hunt, 2010; Frehill, 2012). Asshown in figure 11, AAUW’s analysis of NationalScience Foundation data finds that while most(about 65 percent) women and men who graduatewith bachelor’s degrees in engineering initially takeengineering jobs, by 10 years into <strong>the</strong>ir careers, onlyabout 40 percent of graduates, both women andmen, remain in engineering. While men’s retentionrate levels off at around 40 percent for <strong>the</strong> next 25years, <strong>the</strong> retention rate for women continues todecline. Thirty years into <strong>the</strong>ir careers (by <strong>the</strong> timewomen and men are in <strong>the</strong>ir 50s), women are halfas likely as men to <strong>report</strong> that <strong>the</strong>y are still workingas engineers. 16Retention in computingAs in engineering, women appear to be morelikely than men to leave <strong>the</strong> computing workforce(Hewlett, Buck Luce et al., 2008). Among
AAUW 27FIGURE 11. RETENTION IN ENGINEERING, BY GENDER, 2010706065%64%WomenMenPercentage of engineering graduatesworking in engineering504030201046%43%40%35%37%34%35%30%36%23%39%19%00–45–910–1415–1920–2425–2930–34Years since bachelor’s degree completedNotes: Includes only individuals who <strong>report</strong>ed a bachelor’s degree in engineering and no additional educational credential as of 2010. Includes women and men who <strong>report</strong>edearning a bachelor’s degree in engineering as well as working in an engineering occupation in ei<strong>the</strong>r <strong>the</strong> National Survey of College Graduates or <strong>the</strong> National Survey of RecentCollege Graduates administered in October 2010.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2010a, 2010b).FIGURE 12. RETENTION IN COMPUTING, BY GENDER, 2010Percentage of computing graduatesworking in computing occupations7060504030201057%28%59%45%56%37%52%46%45%43%52%32%WomenMen36%32%00–45–910–1415–1920–24Years since bachelor’s degree completed25–2930–34Notes: The public data used for this analysis combined computing and ma<strong>the</strong>matics occupations and degrees; however, ma<strong>the</strong>matics is typically small compared to computing inboth employment and educational data. Includes individuals who <strong>report</strong>ed earning a bachelor’s degree in computing or ma<strong>the</strong>matics and no additional educational credential andwho were working in a computing or ma<strong>the</strong>matical occupation in ei<strong>the</strong>r <strong>the</strong> National Survey of College Graduates or <strong>the</strong> National Survey of Recent College Graduatesadministered in October 2010.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2010a, 2010b).
28SOLVING THE EQUATIONPatentingDescribing women’s participation in engineering andcomputing overall is important, but it is also importantto measure how women are participating. Level of participationis more difficult to measure than simple representation,but patenting is one metric of innovationand influence in engineering and computing.A patent is a set of exclusive rights granted by a governmentto an inventor to manufacture, use, or sell aninvention for a certain number of years. A recent analysisof patenting rates found that only 5.5 percent ofcommercialized patents are held by women. This disparitywas found to be due in large part to women’sunderrepresentation in engineering, specifically electricaland mechanical engineering, which are <strong>the</strong> mostpatent-intensive STEM fields and fields in which womenare particularly underrepresented (Hunt et al., 2012).A recent analysis of patenting in computing found thatbetween 1980 and 2010, only 13 percent of U.S.-inventedcomputing patents had at least one female inventor.Although overall patenting rates for women in computinghave been and remain quite low, <strong>the</strong> trend ispositive, with more women being awarded patents forcomputing inventions in recent years compared with<strong>the</strong> past. Importantly, this increase happened during aperiod in which women’s participation in computing wasdeclining, which makes <strong>the</strong> increase particularly noteworthy(National Center for Women and InformationTechnology, 2012).None<strong>the</strong>less, while <strong>the</strong> trend is positive, <strong>the</strong> large gendergap in patenting suggests that women are significantlyless well represented among those doing leading-edgework in engineering and computing than in<strong>the</strong>se fields overall (Rosser, 2012).FIGURE 13. FEMALE FACULTY, BY RANK AND DISCIPLINE, 2004 AND 2013Percentage of faculty who are women3025201510516%26%12%20%10%13%18%23%12%17%6%200420139%0AssistantAssociateFullAssistantAssociateFullCOMPUTING PROFESSORSENGINEERING PROFESSORSSource: L. M. Frehill analysis of data from American Society for Engineering Education (2005, 2014) and Computing Research Association (2005, 2014).
AAUW 29all workers with bachelor’s degrees in computingin 2010, 54 percent of men were working in <strong>the</strong>computing field, whereas only 43 percent of womenwere (National Science Board, 2014, appendixtable 3-15). Unlike engineering, however, a genderdifference in participation appears to start immediatelyafter college graduation. AAUW’s analysis ofNational Science Foundation data (2010a, 2010b)finds that in <strong>the</strong> first few years after earning computingdegrees, 28 percent of women and 57 percentof men <strong>report</strong> working in a computing job (seefigure 12). Men with bachelor’s degrees in computingare less and less likely to work in a computingoccupation <strong>the</strong> far<strong>the</strong>r away from graduation <strong>the</strong>yget, while women who graduated longer ago aresometimes more likely to be in a computing ormath occupation than women who graduated morerecently. The trends shown in figure 12 suggest noeasy explanation for computing retention rates formen or women. The volatility of <strong>the</strong> computinglabor market and <strong>the</strong> rapid pace of technologicalchange may be important in understanding <strong>the</strong>career paths of both women and men with bachelor’sdegrees in computing.Glass and colleagues (2013) found that womenworking in engineering and computing are morelikely to leave <strong>the</strong>ir occupational fields than arewomen in o<strong>the</strong>r fields. After about 12 years,50 percent of women who originally worked inSTEM, predominantly engineering and computing,had exited and were employed in o<strong>the</strong>r fields.In contrast, only about 20 percent of womenprofessionals in o<strong>the</strong>r fields, such as management,financial operations, and nursing, exited <strong>the</strong>ir professionaloccupation throughout <strong>the</strong> course of <strong>the</strong>study, which spanned almost 30 years. The disparityin retention between STEM and non-STEMprofessionals was found to be almost entirely due toSTEM women switching out of STEM fields butnot out of <strong>the</strong> labor force.The academic workforceAs educators of <strong>the</strong> next generation of engineersand computing professionals, faculty at U.S. collegesand universities represent a critical part of<strong>the</strong> engineering and computing workforce. Collegeprofessors are role models, influencing who enters<strong>the</strong> field and who succeeds in it. Women andpeople of o<strong>the</strong>r underrepresented groups are scarceon engineering and computing faculties at U.S.colleges and universities, with white and Asianmen making up <strong>the</strong> large majority of faculty at alllevels and most dominant among full professors(see figures A9 and A10 in <strong>the</strong> appendix). Womenare better represented at <strong>the</strong> entry level of assistantprofessor (23 percent in engineering and 26 percentin computing) than at <strong>the</strong> typically tenured associatelevel (17 percent in engineering and 20 percentin computing) and <strong>the</strong> more-senior full level (9percent in engineering and 13 percent in computing).(Some of <strong>the</strong>se numbers do not match whatMethodologyThis <strong>report</strong> is based on a review of academic literature,expert advice from an advisory panel,and interviews with leading researchers. The literaturereview spanned numerous fields, includingpsychology, computing, organizational behavior,management, sociology, economics, and engineeringeducation, among o<strong>the</strong>rs. Using multiple databases,including ProQuest Summon, Web of Science,Psycinfo, and JSTOR, AAUW reviewed more than 750publications, most written within <strong>the</strong> past 15 years,related to <strong>the</strong> topic of women in engineering andcomputing occupations and focusing on empiricalresearch with practical applications.AAUW identified key, recurrent <strong>the</strong>mes in <strong>the</strong> literatureand, along with a team of expert advisersdrawn from academia, industry, associations,and government, selected research findings thathave been published or accepted for publicationin a peer-reviewed research venue within <strong>the</strong> last15 years to feature in <strong>the</strong> <strong>report</strong>. These findings,described in chapters 3 through 9, shed light onfactors contributing to <strong>the</strong> underrepresentation ofwomen in engineering and computing. In addition<strong>the</strong> findings have <strong>the</strong> potential to increase publicunderstanding and influence policies and practicesrelated to this issue.
30SOLVING THE EQUATIONis shown in figures A9 and A10 exactly because ofrounding corrections.) This trend holds true withinracial/ethnic groups as well, with a substantial portionof <strong>the</strong> few URM women who obtain entrylevelfaculty positions in engineering and computingnot advancing through <strong>the</strong> ranks (Hess, C., etal., 2013). Black, Hispanic, and American Indian/Alaska Native (URM) women toge<strong>the</strong>r make upjust 1 percent of full professors in engineering and0.4 percent of full professors in computing (seefigures A9 and A10).The representation of women on engineeringand computing faculties has increased over time(see figure 13). Among full professors, for example,women’s representation grew from 10 percent to13 percent in computing and from 6 percent to 9percent in engineering from 2004 to 2013. At <strong>the</strong>entry level of assistant professor, <strong>the</strong> representationof women among college and university faculty hasincreased in computing (from 16 percent in 2004to 26 percent in 2013) and in engineering (from 18percent in 2004 to 23 percent in 2013) during <strong>the</strong>past decade. 17 The percentages remain low, however,and many engineering and computing studentslikely still never have a female professor.SummaryGirls are graduating from high school on nearlyequal footing with boys in terms of math andscience achievement. Yet young women—acrossracial/ethnic lines—pursue engineering andcomputing in much smaller numbers than youngmen do. By college graduation, women are greatlyoutnumbered by men in every engineering andcomputing discipline. Likewise, in <strong>the</strong> workforcewomen are dramatically underrepresented in <strong>the</strong>fields of engineering and computing and are morelikely to leave <strong>the</strong>se fields than <strong>the</strong>ir male counterpartsare. At <strong>the</strong> faculty level, women remainunderrepresented, especially in more-senior positions.The next chapter explores <strong>the</strong> reasons behind<strong>the</strong>se gender differences.Chapter 1 notes1. In this <strong>report</strong> STEM refers to <strong>the</strong> physical, biological, and agricultural sciences; computer and information sciences; engineeringand engineering technologies; and ma<strong>the</strong>matics, unless o<strong>the</strong>rwise noted. The social and behavioral sciences, such as psychologyand economics, are not included, nor are health workers, such as doctors and nurses.2. AAUW’s analysis of U.S. Department of Labor, Bureau of Labor Statistics (2014e) data finds that “computer and informationresearch scientist” is <strong>the</strong> only computing or engineering occupation of 31 such occupations to require education beyond abachelor’s degree for an entry-level position. Computer and information research scientists make up less than 1 percent of <strong>the</strong>engineering and computing workforce.3. AAUW’s analysis of U.S. Department of Labor, Bureau of Labor Statistics (2014e) data finds that nine of 27 science and mathoccupational categories require education higher than a bachelor’s degree for an entry-level position. These categories includeanimal scientists, physicists, astronomers, biochemists and biophysicists, medical scientists, hydrologists, epidemiologists, ma<strong>the</strong>maticians,and statisticians. Professionals in <strong>the</strong>se occupations make up approximately 28 percent of <strong>the</strong> scientific and ma<strong>the</strong>maticalworkforce.4. Although <strong>the</strong> U.S. Census Bureau’s 2010 American Community Survey <strong>report</strong>ed that 4 percent of workers (and 6 percent ofcomputer, engineering, and science workers) “usually” worked from home in 2010, <strong>the</strong> Census Bureau’s 2010 Survey of Income andProgram Participation (SIPP) <strong>report</strong>ed that 10 percent of workers worked from home “at least one full day a week” in a typicalmonth. Both sources find that more and more workers are working from home over time (Mateyka et al., 2012).
5. In this <strong>report</strong>, AAUW defines engineering professions to include those that fall under <strong>the</strong> 17-2000 “engineers” category of <strong>the</strong>2010 Standard Occupational Classification (U.S. Department of Labor, Bureau of Labor Statistics, 2009). Engineering technicia<strong>nsa</strong>nd drafters are not included in this definition. See figure A1 for <strong>the</strong> full list.6. In this <strong>report</strong>, AAUW defines computing occupations to include those that fall under <strong>the</strong> 15-1100 “computer occupations”category of 2010 Standard Occupational Classification (U.S. Department of Labor, Bureau of Labor Statistics, 2009). See figure A2for <strong>the</strong> full list.7. Throughout this <strong>report</strong>, AAUW defines academic computing disciplines as those included in <strong>the</strong> 2010 Classification ofInstructional Programs (U.S. Department of Education, National Center for Education Statistics, 2010) “computer and informationsciences and support services” category (CIP code 11), with <strong>the</strong> exception of CIP code 11.06, data entry, which <strong>the</strong> NationalScience Foundation does not include in <strong>the</strong> “detailed” field of computer science. Likewise, <strong>the</strong> academic engineering discipline isdefined to include <strong>the</strong> fields included in CIP code 14, with <strong>the</strong> exception of CIP code 14.3701, “operations research,” which NSFincludes in <strong>the</strong> field of business and management. Engineering also includes three additional disciplines that NSF includes inits category of engineering: engineering/industrial management (CIP code 15.1501), geographic information science (CIP code45.0702), and materials science (CIP code 40.1001). For a list of academic disciplines included in engineering and computing inthis <strong>report</strong>, see figures A11 and A12 in <strong>the</strong> appendix.8. The number of bachelor’s degrees in computer science awarded to women has dropped not only in percentages but in actualdegrees awarded as well. In 1986 slightly more than 15,000 women earned bachelor’s degrees in computer science (NationalScience Foundation, Division of Science Resource Statistics, 2013), but by 2013 that number had dropped to approximately9,000 bachelor’s degrees (National Science Foundation, National Center for Science and Engineering Statistics, 2014a). On <strong>the</strong>o<strong>the</strong>r hand, men earned more bachelor’s degrees (slightly more than 42,000) in computing in 2013 than in past decades. The onlyyear in which men earned more computing bachelor’s degrees than 2013 was 2004, when nearly 45,000 men earned computingdegrees (National Science Foundation, Division of Science Resources Statistics, 2013).9. These numbers may underestimate <strong>the</strong> total number of environmental engineering degrees awarded because some civil andchemical engineering programs also may have an environmental focus. The environmental engineering bar shown in figure 7includes only those 2013 bachelor’s degrees <strong>report</strong>ed to <strong>the</strong> U.S. Department of Education, National Center for EducationStatistics awarded in <strong>the</strong> primary field of environmental/environmental health engineering.10. Although engineering is a much bigger field than computing, more black women and men earned degrees in computer sciencethan in engineering in 2013. The reverse, and more expected, scenario is true for Hispanic/Latinos and American Indians/AlaskaNatives, who were awarded more degrees in engineering than in computer science (see figure A5 in <strong>the</strong> appendix).11. Figures A13 and A14 in <strong>the</strong> appendix show <strong>the</strong> percentage of master’s and doctoral degrees awarded to women from 1970 to2013 in all fields, all science and engineering fields, engineering, and computing.12. Computer user support specialists and web developers are two fast-growing computing occupations that require an associatedegree or less for an entry-level position (U.S. Department of Labor, Bureau of Labor Statistics, 2014e).13. Computing occupations with large numbers of workers who hold bachelor’s degrees or higher in computing majors include computersoftware engineers and applications and systems software developers (AAUW analysis of National Science Board, 2014,appendix table 3-3).14. Computing occupations with large numbers of workers who hold bachelor’s degrees but not computing or engineering degreesare web developers and database administrators (AAUW analysis of National Science Board, 2014, appendix table 3-3).15. Minority-serving institutions, including historically black colleges and universities, Hispanic-serving institutions, and tribalcolleges and universities, have a strong history of graduating relatively high percentages of female STEM majors, including computingmajors, who go on to earn doctorates. The persistence of women in computing in <strong>the</strong>se institutions has been attributed, inpart, to nurturing environments, faculty who believe in <strong>the</strong>ir students, and a collaborative peer culture (Ong, 2011).16. Gender differences in retention in <strong>the</strong> engineering workforce may vary based on engineering discipline. Some evidence suggeststhat attrition rates from <strong>the</strong> workforce are similar for women and men in civil engineering but that women are less likely thanmen to stay in mechanical and electrical/computer engineering fields (Frehill, 2010).17. The faculty data shown in figure 13 and in figures A9 and A10 in <strong>the</strong> appendix have an important caveat: They represent dataonly from <strong>the</strong> institutions that replied to <strong>the</strong> 2013 American Society for Engineering Education (2005, 2014) and Taulbeesurveys (Computing Research Association, 2005, 2014). The ASEE numbers include data from institutions that award approximately95 percent of engineering bachelor’s degrees, so <strong>the</strong>y are a good representation of <strong>the</strong> field of engineering professors. Forcomputing, <strong>the</strong> caveat is more important. CRA data include only institutions that confer doctoral degrees, meaning that figures13 and A10 show data for <strong>the</strong> faculty at 143 institutions that toge<strong>the</strong>r award only about one-fourth of computing bachelor’sdegrees, since many computer science degrees are awarded by institutions that do not offer doctoral degrees, including many forprofitinstitutions.AAUW 31
Chapter 2.Why So Few?
34SOLVING THE EQUATIONStudy after study finds that women have ability,good grades, and high test scores in STEMsubjects, and yet women are turning away, or beingpushed away, from engineering and computingfields. A <strong>the</strong>me that overarches much of <strong>the</strong>research on this topic is that women often feel asif <strong>the</strong>y don’t fit or belong in <strong>the</strong>se fields. Researchinto this perceived lack of fit provides a complexpicture of social and environmental factors influencingand interacting with individual motivatio<strong>nsa</strong>nd values that are, in turn, also influencedby <strong>the</strong> wider culture. This chapter describes <strong>the</strong>latest research on structural and cultural factors inengineering and computing that may contribute towomen’s underrepresentation in <strong>the</strong>se fields.Structural and culturalbarriersAs past decades have shown, simply trying torecruit girls and women into existing engineeringand computing programs and workplaces hashad limited success. Catalyst (2014) found thatwomen in business roles at technical companies,like women in technical roles at <strong>the</strong>se companies,tend to leave at higher rates than <strong>the</strong>ir male peersdo (53 percent of women compared with 31 percentof men after <strong>the</strong>ir first post-MBA job). Thisfinding suggests that <strong>the</strong> overall workplace cultureand environment in technical industries may notbe working for women, whe<strong>the</strong>r or not <strong>the</strong>y arein technical roles. In both college and workplaceenvironments, institutional structures and practicesand more general cultural factors may contribute to<strong>the</strong> underrepresentation of women in engineeringand computing fields.Narrow focusOne significant impediment, according to somescholars, is an emphasis on logical thinking at <strong>the</strong>expense of critical thinking in engineering culture(Claris & Riley, 2012). Scholars have pointed to aculture in engineering that discourages thinkingbeyond <strong>the</strong> technical parameters of a given problem(Cech, 2014). Engineering students, for example,are rarely asked to reflect on what <strong>the</strong>y do, why <strong>the</strong>ydo it, and what <strong>the</strong> implications might be (Baillie& Levine, 2013). Specifically, <strong>the</strong> “culture of disengagement”in many college engineering programsdoes a poor job of training future engineers in <strong>the</strong>irethical and social responsibilities and cultivates anunderstanding of nontechnical concerns, such as<strong>the</strong> public welfare, as irrelevant to “real” engineeringwork (Cech, 2014). These elements of engineeringculture are not confined to <strong>the</strong> college environmentbut persist once engineers enter <strong>the</strong> workforce,and although <strong>the</strong>y are likely to discourage manywomen and men from pursuing engineering, <strong>the</strong>yare perhaps especially discouraging for womenbecause women are more likely than men to expressa preference for work with a clear social purpose(Konrad et al., 2000).Engineering and computing programs typicallyinclude high course load and workload requirements.Engineering curricula, in particular, areoften rigid, making it difficult for students totransfer into engineering if <strong>the</strong>y do not start outin it. One study attests to this difficulty, findingthat 90 percent of those studying engineering in<strong>the</strong>ir eighth semester in college had identifiedengineering as <strong>the</strong>ir major when <strong>the</strong>y began college(Ohland et al., 2008). Among students with strongma<strong>the</strong>matical aptitude (including most engineeringand computing students), women are morelikely than men to also have strong verbal aptitude(Wang et al., 2013). The constrained curriculumin engineering and computing may make it difficultfor students to take elective courses in o<strong>the</strong>rfields or take advantage of o<strong>the</strong>r extracurricularopportunities that can be valuable contributors to<strong>the</strong> college experience, especially for students withbroad interests and aptitudes. For example, onestudy found that students majoring in engineeringand computing were <strong>the</strong> least likely of all studentsto take foreign language courses or participate instudy abroad programs (Lichtenstein et al., 2010).IsolationWomen in engineering and computing fields often<strong>report</strong> isolation, a lack of voice, and a lack of support(Ayre et al., 2013; Fouad et al., 2012; Hewlett,Buck Luce et al., 2008; Hewlett, Sherbin et al.,2014; Servon & Visser, 2011). A study of womenand men working in technology at 21 high-tech
AAUW 35companies found that women were less likely thanmen to indicate that <strong>the</strong>ir supervisors were receptiveto suggestions, less likely to say that <strong>the</strong>irsupervisors were available when <strong>the</strong>y needed <strong>the</strong>m,and less likely to agree that “it is safe to speak upmost of <strong>the</strong> time” (Catalyst, 2008). In one studyof women in private-sector technical jobs, a thirdsaid that <strong>the</strong>y felt extremely isolated at work. In<strong>the</strong> same study, four of 10 female engineers andcomputing professionals <strong>report</strong>ed lacking rolemodels, while about half <strong>report</strong>ed lacking mentors(Hewlett, Buck Luce et al., 2008).Stereotypical surroundingsThe physical environment in engineering orcomputing classrooms and workplaces can makea difference in how comfortable women find <strong>the</strong>environment. In one study, female students whoentered a room containing stereotypical “geek”objects were less likely to identify <strong>the</strong>mselves withcomputing or feel <strong>the</strong>y belonged with a companyor on a team (even an all-female team) than didwomen who entered a room containing genderneutralobjects (Cheryan et al., 2009).Social networks less helpfulfor womenSocial networks appear to be less beneficial forwomen than for men, perhaps especially in computing.An analysis of social networks amongundergraduate management information systems(MIS) students as <strong>the</strong>y searched for jobs foundthat although social networks improved individuals’job prospects, women’s social networks did notprovide <strong>the</strong> job opportunities that men’s networksdid (Koput & Gutek, 2010). For example, one malestudent who had a C average, many male contacts,and very few female contacts participated in 16job interviews, received five job offers, and startedhis career with a high salary. In contrast, womenin <strong>the</strong> study, who also had many male contacts,generally did not get many job interviews or endup with high salaries, even if <strong>the</strong>y had high grades.According to Koput and Gutek (2010, p. 71),“Women high in aspects of human capital, socialcapital, or both did not fare as well as might havebeen anticipated or as well as seemingly similarmen.” The same study found that women withstrong ties to men were more likely than o<strong>the</strong>rwomen to be seen as legitimate in <strong>the</strong> technical,male-dominated MIS field. O<strong>the</strong>r research hasidentified social networks of powerful men, fromwhich women are excluded, as barriers for womenin engineering workplaces (Faulkner, 2009a).Work-life balance issuesWork-life balance is an important issue for workers,especially women, in engineering and computing.Some researchers argue that ra<strong>the</strong>r than work-lifebalance, <strong>the</strong> real issue is a “culture of overwork.”Organizational cultures of overwork result in dissatisfactionamong women and men (Padavic &Ely, 2013). Because culturally women are expectedto fulfill <strong>the</strong> responsibilities associated with homeand family and men are expected to be <strong>the</strong> breadwinners,women may experience negative outcomesas a result of this culture of overwork more frequentlythan men do. For example, a survey of midlevelscientists and engineers in high-tech companiesfound that women were more likely than mento suffer poor health and to delay or forgo gettingmarried and having children as a result of workdemands (Simard et al., 2008). When employersin male-dominated fields such as engineeringand technology expect employees to work longhours (more than 50 hours per week), women withchildren are much more likely than men or childlesswomen not only to leave <strong>the</strong>ir employer but toexit <strong>the</strong> paid workforce entirely (Cha, 2013). Thisresearch suggests that when work responsibilitiesbecome incompatible with <strong>the</strong> demands of familylife, women, especially mo<strong>the</strong>rs, find <strong>the</strong>mselvesin a situation in which <strong>the</strong>y must choose betweenwork and family.Relatively little research has explored whywomen leave engineering and computing fields.One study found that half of women who leftcorporate science, engineering, and technologyjobs moved to technical jobs outside <strong>the</strong> corporatesector, and <strong>the</strong> rest moved to jobs outside STEMfields altoge<strong>the</strong>r (Hewlett, Buck Luce et al., 2008).Preston (2004) identified a lack of mentoring, amismatch of interests, and difficulty balancing work
36SOLVING THE EQUATIONand family responsibilities as reasons for leaving.Frehill (2012) found that women were more likelythan men to cite a “change in career or professionalinterests” as <strong>the</strong> most important reason <strong>the</strong>y leftengineering. The first study to comprehensivelyinvestigate factors related to women’s decisions toleave or stay in engineering careers (Fouad et al.,2012) is described in chapter 9. It identifies factorssuch as work environment and access to trainingand development as key to women’s decisions tostay in or leave <strong>the</strong>ir engineering jobs.Challenging academic workplacesWomen working in academic engineering andcomputing jobs face challenges similar to thoseof o<strong>the</strong>r women working in engineering andcomputing. Women in academic STEM environments<strong>report</strong> lower job satisfaction than <strong>the</strong>ir malecounterparts do (National Research Council, 2010;Bilimoria et al., 2008), although some evidencesuggests that this gender difference in satisfactionhas disappeared among engineering and computingfaculty in recent years (Ceci et al., 2014).Personal experiences with sexual harassment orgender discrimination are <strong>the</strong> most likely factorsto affect job satisfaction (Settles, Cortina, Malley,et al., 2006), but studies have also connected <strong>the</strong>general workplace climate—including perceptionsof more work-family interference, less support,gender mistreatment, and an overall impression of<strong>the</strong> workplace as more competitive and hostile—tolower job satisfaction (Settles, Cortina, Malley etal., 2006; Marchetti et al., 2012). In one study of765 STEM faculty members, women ranked <strong>the</strong>irworkplace environment more negatively than mendid in six of eight measures, considering it moreformal, less exciting, less helpful, less creative, morestressful, and less inclusive. Women in academicscience and engineering also <strong>report</strong>ed fewer conversationswith colleagues about research, loweraccess to human and material resources, and lowerrecognition of accomplishments (Fox, 2010).Some researchers have suggested that genderdiscrimination in academia is a thing of <strong>the</strong> pastand that <strong>the</strong> remaining obstacles to women’s fullparticipation in academic STEM fields are workfamilybalance and pre-college decisions that resultin fewer women majoring in fields such as engineeringand computing (Ceci et al., 2014). Indeed,as described in chapter 1, women are less likelythan men to start college intending to major inengineering or computing, and efforts to encouragegirls’ interest in STEM subjects may prove usefulin increasing <strong>the</strong> representation of women in <strong>the</strong>sefields. Likewise, some telling statistics point to<strong>the</strong> difficulties that mo<strong>the</strong>rs still face in academicenvironments. Mason and Goulden (2002) foundthat among science professors who became parentswithin <strong>the</strong> first five years after receiving a doctorate,77 percent of <strong>the</strong> men but only 53 percent of<strong>the</strong> women had achieved tenure 12 to 14 years afterearning a doctorate. These numbers support <strong>the</strong>contention that work-family balance is an obstacleto women’s full participation in academic STEMworkplaces. Ra<strong>the</strong>r than showing that gender discriminationno longer exists in academia, however,<strong>the</strong>se numbers may point to environments withpolicies and structures that make it difficult forwomen with children to thrive.While research has found that women whoapply for tenure-track positions in math-intensivefields are as likely as <strong>the</strong>ir male peers to receiveoffers, qualified women are less likely than <strong>the</strong>irmale peers to apply for <strong>the</strong>se positions (NationalResearch Council, 2010), perhaps because <strong>the</strong>yperceive academic settings as environments thatwill not support <strong>the</strong>m in achieving <strong>the</strong>ir life goals.Research described in chapter 3 demonstrates that,far from being a thing of <strong>the</strong> past, gender bias inhiring is alive and well in academic environments(Moss-Racusin, Dovidio et al., 2012a).Asian, black, Hispanic, and American Indian/Alaska Native women in academic STEM departmentsface additional challenges. Collecting andaggregating data on specific fields for women ofcolor is difficult because of <strong>the</strong> low numbers, butwomen of color in academic science and engineeringdepartments generally <strong>report</strong> different, andmore substantial, stress than do o<strong>the</strong>r demographicgroups. For example, women and men of color aremore likely to <strong>report</strong> <strong>the</strong> stress of struggling withpersonal finances, women of color <strong>report</strong> morestress than both white women and men of colorin lack of personal time and managing household
AAUW 37duties, and women of color <strong>report</strong> <strong>the</strong> most stressfrom discrimination. Additionally, 79 percent ofwomen of color responded affirmatively to <strong>the</strong>statement “I need to work harder to be perceivedas a legitimate scholar,” compared with 67 percentof white women, 60 percent of men of color, and52 percent of white men (National Academy ofSciences, 2013).Stereotypes and biasesStereotypes and biases are important cultural factorsthat may influence women’s representationin engineering and computing. A stereotype is anassociation of specific characteristics with a group(Dovidio et al., 2010). Stereotypes can be descriptive(what women and men are like) or prescriptive(what women and men should be like). Everyoneuses stereotypes to process new informationquickly, assess differences between individuals andgroups, and make predictions. Stereotypes allow usto use fewer cognitive resources than we would ifwe made individual observations each time we metsomeone new (Heilman & Eagly, 2008; Heilman,2012). Indeed, human beings have been describedas “cognitive misers” who are reluctant to engage ineffortful thought unless absolutely necessary (Fiske& Taylor, 1991). For this reason, stereotypes arevery powerful and difficult to override, and <strong>the</strong>ycan lead to biased behavior or discrimination whenwe view members of a group based on <strong>the</strong>ir groupstatus ra<strong>the</strong>r than as individuals (Heilman, 2012;Dovidio et al., 2010).Gender stereotypes tend to place greater socialvalue on men and evaluate men’s competence asgreater than women’s (Ridgeway 2001). One specificarea in which men are stereotypically deemedmore competent than women is ma<strong>the</strong>matics.Parents’ and teachers’ expectations for children’sma<strong>the</strong>matical achievement are often gender-biasedand can influence children’s attitudes toward math(Gunderson et al., 2012; Varma, 2010). Parents’ andteachers’ own feelings about math can rub off onchildren. In one study, no relationship was foundbetween first and second grade female teachers’math anxiety and <strong>the</strong>ir students’ math achievementat <strong>the</strong> beginning of <strong>the</strong> school year. By <strong>the</strong>school year’s end, however, <strong>the</strong> more anxious femaleteachers were about math, <strong>the</strong> more likely girls (butnot boys) in <strong>the</strong>ir class were to endorse <strong>the</strong> commonlyheld stereotype that “boys are good at math,and girls are good at reading” and <strong>the</strong> lower <strong>the</strong>segirls’ math achievement was (Beilock et al., 2010).Warmth versus competenceWhile men are stereotypically thought of as competentin many domains, women are stereotypicallyconsidered to be warm. Competence and warmthare traits that we tend to immediately assign topeople we meet, and <strong>the</strong>se traits are often perceivedto be in opposition to each o<strong>the</strong>r (Holoien& Fiske, 2013). Because competence is valued inengineering and computing, <strong>the</strong> requirements forbeing viewed positively as a technical professionaland being viewed positively as a woman are oftenconflicting. As a result, many women in technicalroles <strong>report</strong> difficulty forging strong identities asengineers or computing professionals (Hatmaker,2013; Faulkner, 2007, 2009a, 2009b; Ayre et al.,2013), and many female engineers describe anincreased pressure to prove <strong>the</strong>mselves (Hatmaker,2013; Smith, L., 2013). When women emphasize<strong>the</strong>ir competent characteristics and effectivenessat work, <strong>the</strong>y often experience backlash for violating<strong>the</strong> gender stereotype that women are warm,and <strong>the</strong>y are seen as less likeable than men whoemphasize <strong>the</strong> same behaviors, especially in maledominatedfields (Phelan et al., 2008; Rudman &Phelan, 2008; Heilman, Wallen et al., 2004). On<strong>the</strong> o<strong>the</strong>r hand, women seen as warm but not competentare less likely to be respected and more likelyto be pitied and socially neglected in <strong>the</strong> workplace(Fiske, 2012; Cuddy et al., 2007).MicroinequitiesBy <strong>the</strong> time women begin formal engineering orcomputing training in college, <strong>the</strong>y likely haveencountered gender-biased behavior on manyoccasions. Microinequities have been described as“apparently small events … frequently unrecognizedby <strong>the</strong> perpetrator … which occur whereverpeople are perceived to be ‘different’” (Rowe, 2008,p. 45). Examples include facial expressions, gestures,tone of voice, and subtle actions, such as assigning<strong>the</strong> role of note taker to a woman ra<strong>the</strong>r than a
38SOLVING THE EQUATIONman. Accumulated over time, <strong>the</strong>se microinequitiescan affect students’ self-concept, which may, inturn, influence <strong>the</strong>ir choice of a career (Rowe, 1990;Bandura, 1997).Camacho and Lord (2011) found that femaleengineering undergraduates frequently encountergender-based “microaggressions,” small discriminatorybehaviors of mostly nonphysical aggression(Pierce, 1970), in <strong>the</strong> engineering educationenvironment. Such behaviors include encounteringsurprise that a woman would be interested in engineering,having male students interrupt or speakover <strong>the</strong>m, experiencing difficulty having <strong>the</strong>ir ideasheard, being exposed to sexual discussions andjoking, hearing suggestions that women are in <strong>the</strong>department only as a result of affirmative actionpolicies ra<strong>the</strong>r than because of <strong>the</strong>ir achievementsand abilities, and hearing gendered statements byprofessors during lectures. O<strong>the</strong>r research indicatesthat microinequities persist long after women enter<strong>the</strong> engineering workforce (Faulkner, 2009b).Microinequities illustrate how discriminationin school and <strong>the</strong> workplace is often subtleand not overt in its intent to harm (Hebl et al.,2002). None<strong>the</strong>less, microinequities may result inincreased stress and feelings of exclusion amongwomen in engineering (Camacho & Lord, 2011).Explicit and implicit biasBiases can be explicit (conscious and self-<strong>report</strong>edon surveys or in interviews) or implicit (operatingautomatically, typically outside an individual’sconscious awareness). Explicit gender bias has beensteadily declining for decades. Whe<strong>the</strong>r due to agenuine increase in egalitarian beliefs or to a greaterhesitation to express biased attitudes (or some combinationof <strong>the</strong> two), people are less likely today tosay that <strong>the</strong>y hold biased beliefs than <strong>the</strong>y were in<strong>the</strong> past (Banaji & Greenwald, 2013). In contrast,implicit gender biases remain pervasive (Nosek,Banaji et al., 2002b; Lane et al., 2012; Smyth,Greenwald et al., 2015; Dovidio, 2001). Even individualswho consciously reject gender stereotypesoften still hold implicit gender biases. In sociallysensitive domains involving topics such as race orgender, some evidence indicates that implicit biasis a better predictor of behavior and judgment thanGamergateAggression against women is not always subtle.The Gamergate controversy of 2014 dramaticallyillustrates <strong>the</strong> virulence of gender bias in <strong>the</strong> videogaming industry. The controversy began with an exboyfriend’spostings about <strong>the</strong> journalistic ethicsof his ex-girlfriend, a prominent game developer.These allegations led to an intense flurry of postingsin online forums and on social media, whichquickly devolved into sexist attacks against <strong>the</strong>female gamer and against women in <strong>the</strong> industry.Female gamers were barraged by hostile postingsand messages, and some were subjected to threatsof rape and death that resulted in <strong>the</strong> women fleeing<strong>the</strong>ir homes. One even received bomb threatsas a result of her work as a feminist critic of gaming.Gamergate is a chilling example of <strong>the</strong> seriousonline and real-world harassment and aggressionthat some women face in traditionally male technicalrealms.is explicit bias (Greenwald, Poehlman et al., 2009).Implicit and explicit biases are related to each o<strong>the</strong>rbut understood by psychologists to operate via distinctand different psychological mechanisms (DeHouwer et al., 2009; Nosek & Smyth, 2007; Nosek,2007). Because implicit bias is widespread and <strong>the</strong>prevalence of explicit bias is declining, this chapterfocuses more on implicit bias.The concept of implicit bias was introducedin 1995, when social psychologists AnthonyGreenwald and Mahzarin Banaji built on <strong>the</strong> psychologicalconcept that our actions are not alwaysunder our conscious control. They argued thatmuch of our behavior is driven by stereotypes thatoperate automatically and, <strong>the</strong>refore, unconsciously.Researchers <strong>the</strong>orize that, starting at an early age,we acquire implicit biases simply by living in a societywhere different types of people fill different rolesand jobs (Cvencek, Greenwald et al., 2011; Cvencek& Meltzoff, 2012). Passive exposure to widespreadbeliefs registers <strong>the</strong>se beliefs in our minds withoutour even knowing it. For this reason, implicit
AAUW 39attitudes and beliefs may be better described asreflections of <strong>the</strong> surrounding environment ra<strong>the</strong>rthan personal attributes (Dasgupta, 2013).Once in place, implicit biases lead us to seekevidence that supports <strong>the</strong>m and question or disregardevidence that contradicts <strong>the</strong>m (Schmader,2013). When we encounter ano<strong>the</strong>r person, weinstantly view her or him as a woman or man, andour views of any o<strong>the</strong>r characteristics that personmay have are shaped by our beliefs about what sheor he is and should be like as a woman or a man(Hassan & Hatmaker, 2014; Ridgeway, 2009). Forexample, a number of qualitative studies conductedin engineering workplaces found that womenare often not seen by <strong>the</strong>ir co-workers and colleaguesas full-fledged members of <strong>the</strong> engineeringprofession (Tonso, 2007; McIlwee & Robinson,1992; Faulkner, 2009b)—<strong>the</strong>y are “highly visible aswomen yet invisible as engineers” (Faulkner, 2009b,p. 169).In 1998 Greenwald and his colleagues introduced<strong>the</strong> Implicit Association Test (IAT), a measuredesigned to detect <strong>the</strong> strength of a person’sautomatic association between two concepts. Todaymany IATs are freely available online at implicit.harvard.edu. One IAT that is especially relevantto women in engineering and computing is <strong>the</strong>gender-science IAT, which measures <strong>the</strong> strengthof associations between gender and science. Usinga computer, participants quickly sort words in eachof two conditions: a gender-stereotypical conditionand a counter-stereotypical condition. In<strong>the</strong> stereotypical condition, subjects use <strong>the</strong> samekeyboard key to categorize items representing male(for example, <strong>the</strong> word “fa<strong>the</strong>r”) and science (forexample, <strong>the</strong> word “physics”) and ano<strong>the</strong>r key tocategorize items representing female (for example,“mo<strong>the</strong>r”) and liberal arts (for example, “literature”).Next, individuals categorize <strong>the</strong> same words pairedin a counter-stereotypical way, for example, maleand liberal arts sorted with one key and female andscience items with a different key. Which conditionis presented first is randomly varied across participants.A participant’s score is based on <strong>the</strong> differencein <strong>the</strong> speed and accuracy of sorting between<strong>the</strong> two conditions.Both women and men, on average, have a strongtendency on <strong>the</strong> IAT to more readily associate malewith science and female with humanities than <strong>the</strong>reverse (Nosek, Banaji et al., 2002a, 2002b; Smyth,Greenwald et al., 2015), and implicit associationsthat pair boys and men with math have been documentedin <strong>the</strong> United States in children as youngas age 7 (Cvencek, Meltzoff et al., 2011).Guidance counselorsAlthough <strong>the</strong> relationship between gender and vocationalinterests is complicated, evidence suggests thatcareer inventory surveys currently prevalent in highschool academic and career counseling may have a genderbias. Studies have found that <strong>the</strong> RIASEC Inventory,<strong>the</strong> survey most commonly used by career counselorstoday, may be better suited to male students than tofemale students and may lead to different occupationrecommendations for girls and boys (Kantamneni &Fouad, 2011; Armstrong et al., 2010).One study found that a sample of guidance counselorsin Utah perceived <strong>the</strong> values, interests, and qualities ofstudents differently based on gender. Many counselorsalso showed an “alarming” lack of knowledge about engineeringeducational and career paths and were unpreparedto inform students about engineering opportunities(Iskander, 2013). O<strong>the</strong>r research found that someguidance counselors in <strong>the</strong> southwest were very muchaware that women are underrepresented in STEM occupatio<strong>nsa</strong>nd that girls are negatively affected by gendersciencestereotypes (Ross, 2012). Understanding moreabout guidance counselors’ gender biases, knowledgeof engineering and computing careers, and awarenessof <strong>the</strong> influence of gender biases may help identify waysfor <strong>the</strong>m to better help girls make informed educationaland career choices.
40SOLVING THE EQUATIONMost studies that examine <strong>the</strong> practicalimpact of implicit biases as measured by <strong>the</strong> IAThave focused on race and not gender (Banaji &Greenwald, 2013). A few examples of <strong>the</strong> behaviorsfound to be predicted by individuals’ implicitpreference for white people include less comfortand less friendliness when talking with a blackinterviewer than a white interviewer (McConnell& Leibold, 2001), greater readiness to perceiveanger in black faces than white faces (Hugenberg& Bodenhausen, 2003), and greater likelihood tolaugh at racial humor and rate it as funny (Lynch,2010). Green and colleagues (2007) found thatphysicians with greater implicit racial biases favoringwhites recommended optimal treatment foracute cardiac symptoms more often for a whitepatient than for a black patient. These studiesprovide evidence that implicit biases are correlatedwith discriminatory behavior and appear to havereal-world implications.While less research has explored <strong>the</strong> effectsof implicit gender biases as measured with <strong>the</strong>IAT, recent evidence described in chapter 3 findsthat implicit gender-math bias is linked to genderdiscrimination. Gender bias coupled with racial/ethnic bias presents a particularly challengingenvironment for women of color in engineeringand computing (Ong, Wright et al., 2011). Fur<strong>the</strong>rstudy is needed about <strong>the</strong> connection betweenimplicit biases related to women in science andmath as measured with <strong>the</strong> IAT and actual behaviorstoward women in engineering and computing.Biased evaluationsBiased evaluations play an important role in <strong>the</strong>professional opportunities afforded to women.Even before <strong>the</strong> formal application process begins,biased evaluations can affect women’s chances ofgetting a position. One study found that professorsfrom many different fields were less likely torespond to an e-mail informally inquiring aboutresearch opportunities from a prospective applicantto a doctoral program if it had a woman’s name onit (Milkman et al., 2014).Hiring situations are particularly vulnerable tobias because hiring managers are generally workingwith limited information under time constraints,and employers typically have little opportunityto reconsider a decision after it has been made(Bendick & Nunes, 2012). Once applicants reach<strong>the</strong> interview stage, women in typically male fieldsface additional challenges, such as negative bodylanguage from interviewers, that can affect interviewperformance (Hess, K. P., 2013).Biased evaluations continue to affect womenonce <strong>the</strong>y have been hired. Female managersreceive lower ratings on performance reviews thanmale managers do and are held to a higher standard,needing better performance ratings than <strong>the</strong>irmale peers to be promoted (Lyness & Heilman,2006). In male-dominated science and engineeringfields women are less likely than men to beseen as experts by <strong>the</strong>ir colleagues and to serve inimportant roles on teams ( Joshi, 2014). Managers’discretion over everyday decisions, such as how toexecute company human resource policies, can beinfluenced by gender biases, resulting in diminishedopportunities for women and increased opportunitiesfor men (Roth & Sonnert, 2011; Ayre etal., 2013; Bobbitt-Zeher, 2011; Catalyst, 2008;Fouad et al., 2012; Williams, C. L., et al., 2012).For example, women are less likely than men tobe granted requests for flexible schedules, and thatlack of workplace flexibility can prevent women,especially working mo<strong>the</strong>rs, from fur<strong>the</strong>ring <strong>the</strong>ircareers (Brescoll, Glass et al., 2013).Castilla and Benard (2010) identified <strong>the</strong>“paradox of meritocracy,” in which managers inorganizations explicitly identified as meritocraticfavor and reward male employees more generouslythan equally qualified female employees. Thisfinding may have particular relevance for engineeringand computing. More engineers and technicalprofessionals, including organizational leaders,may believe that <strong>the</strong>ir workplaces are meritocraticthan do professionals in fields that are less dataoriented.One study of scientists and engineers athigh-tech companies, however, found that womenwere less likely than men to see <strong>the</strong>ir workplaces asmeritocracies, perceiving connections to power andinfluence as necessary for advancement (Simard etal., 2008).
AAUW 41In-group favoritismResearch suggests that biased behavior or discriminationtoday most often results from “in-group”favoritism, or giving preferential treatment to o<strong>the</strong>rswith whom we identify in some way, as opposedto negative treatment of “out-group” members ofgroups with whom we don’t identify. Laboratoryand field studies find that discrimination involving<strong>the</strong> absence of positive treatment happens in manyinstances without any accompanying specificallynegative treatment and is, in fact, more commonthan discrimination that involves outright hostility(Greenwald & Pettigrew, 2014). This researchsuggests that in-group favoritism is an importantmechanism by which unequal group outcomes—including unequal outcomes for women—aremaintained and is, <strong>the</strong>refore, a practice thatindividuals trying to reduce discrimination shouldminimize.Still, if more women moved into leadershiproles in engineering and technical fields, it is possiblethat in-group preferences could result in evenmore women moving into <strong>the</strong>se fields. Kurtulusand Tomaskovic-Devey (2012) found that anincrease in <strong>the</strong> share of female top managers inan organization was associated with subsequentincreases in <strong>the</strong> share of women in mid-level managementpositions in that organization, particularlyfemale managers within <strong>the</strong> same racial/ethnicgroup as that of <strong>the</strong> top managers.SexismSexism can be ei<strong>the</strong>r hostile or “benevolent.” Benevolentsexism is rooted in a belief that women need<strong>the</strong> help and protection of men (Glick & Fiske,1996; Fiske, 2012). Women who are seen as warmbut not competent are especially likely to be <strong>the</strong>recipients of benevolent sexist behaviors such asbeing called “swee<strong>the</strong>art” or being offered help withdangerous aspects of a job. While on <strong>the</strong> surfacebenevolent sexism may seem positive towardwomen, its effects are quite <strong>the</strong> opposite.In one study, participants looked at a jobinterview transcript in which a male interviewershowing hostile sexism (such as a belief thatwomen are incompetent), benevolent sexismEven men are affected bygender biases against womenGender biases can create obstacles not only forwomen in technical workplaces but also for <strong>the</strong>men who work with <strong>the</strong>m. In one study of equallyperforming teams working on a male-typed task,teams with a higher percentage of women ratedboth <strong>the</strong>ir female and male peers’ work more negativelyoverall and expressed less desire to worktoge<strong>the</strong>r in <strong>the</strong> future (West et al., 2012). Ano<strong>the</strong>rstudy found that in a typically male field, peoplerated <strong>the</strong>ir male colleagues as less masculine andless deserving of workplace success if <strong>the</strong>y hadfemale supervisors (Brescoll, Uhlmann et al., 2012).This research sheds light on <strong>the</strong> magnitude of <strong>the</strong>problem of gender bias in predominantly male fieldsand perhaps points to one mechanism by which itis maintained. If men’s work is devalued when menwork with women, men might take steps to avoidworking with women, exacerbating <strong>the</strong> challengesfacing women in male-dominated fields. Whilediversity has demonstrated benefits, <strong>the</strong>re are realchallenges to achieving it.(such as a belief that women should be protected),or no sexism interviewed a female candidate fora stereotypically male job. Researchers found that<strong>the</strong> more participants <strong>report</strong>ed liking <strong>the</strong> sexistinterviewer, <strong>the</strong> less competent and deserving of<strong>the</strong> job <strong>the</strong> participants found <strong>the</strong> candidate (Good,J. J., & Rudman, 2010). Importantly, observersmore frequently liked <strong>the</strong> benevolent sexist than<strong>the</strong> hostile sexist interviewer, and observers neednot have held sexist beliefs <strong>the</strong>mselves to like <strong>the</strong>sexist interviewer.When a leader in an organization is sexist,women can face particularly challenging circumstances.Good and Rudman explain:The more a sexist boss is liked by co-workersand upper level management, <strong>the</strong> lesscompetent female employees may seem as aresult of his sexist treatment. Because benevolentsexism is often not viewed as sexist
42SOLVING THE EQUATION… and in some cases is viewed as positive,chivalrous behavior … it is plausible thatbenevolent sexists are often viewed morefavorably than hostile sexists, as was <strong>the</strong> casein <strong>the</strong> present study. As a result, women maybe especially vulnerable when targeted forbenevolent sexism because <strong>the</strong> perpetratoris often viewed positively, even though histreatment can undermine female recipients.Benevolent sexism has also been shown to resultin women receiving fewer challenging assignments,which can limit career development and advancement(King et al., 2012). Whe<strong>the</strong>r sexist behavior ismore prevalent in engineering or computing workplacesthan elsewhere is not clear. Still, evidenceshows that women experience more sex discriminationin workplaces where <strong>the</strong>y make up less thanone-fourth of <strong>the</strong> workers (Stainback et al., 2011),and research described in chapter 4 finds that menin engineering and computing fields tend to havehigher explicit and implicit gender-science biasesthan do men in o<strong>the</strong>r fields (Smyth, Greenwald etal., 2015).Sexual harassmentSexual harassment, defined broadly as unwelcomeconduct of a sexual nature, can include behaviorssuch as direct and unwanted sexual advances andphysical contact or a hostile work environment thatincludes sexual and sex-based taunting, comments,or denigration (Berdahl, 2007). Sexual harassmentis widespread in engineering and technology(Servon & Visser, 2011; Faulkner, 2009a). Onerecent study of college-educated women in <strong>the</strong>private science, engineering, and technology sectorfound that 63 percent of women in engineeringand 51 percent of women in technology hadexperienced sexual harassment (Hewlett, Sherbinet al., 2014). Organizational climate is a majorfactor in <strong>the</strong> prevalence of sexual harassment in<strong>the</strong> workplace (O’Leary-Kelly et al., 2009). Studiessuggest that male workers in male-dominated fieldsmay harass <strong>the</strong>ir female co-workers as a way toprotect <strong>the</strong>ir territory when <strong>the</strong>y sense that womenare encroaching on male space (Berdahl, 2007;Chamberlain et al., 2008; McLaughlin et al., 2012).Because of its pervasiveness, sexual harassmentcan seem “normal,” and women may hesitate to<strong>report</strong> it, opting instead to employ coping mechanismssuch as tuning it out or thinking of it as anecessary evil. Denissen (2010) documented howwomen in <strong>the</strong> building trades prioritized maintaininggood relationships with <strong>the</strong>ir co-workersabove <strong>report</strong>ing sexual harassment, attempting toignore <strong>the</strong> persistent harassing behavior becauseof possible repercussions. In a study of technologyworkplaces, Hunter (2006) found similar challengesfor women, where female employees chose not to<strong>report</strong> sexual harassment and tried to downplay<strong>the</strong>ir femininity to “fit in.”The consequences of sexual harassment aretangible and troubling. Personal or observed experienceswith sexual harassment or gender discriminationare associated with alienation and low jobsatisfaction (Settles, Cortina, Buchanan et al., 2013;Settles, Cortina, Malley et al., 2006). Women whoare targets of workplace incivility such as sexualharassment are more likely to consider quitting<strong>the</strong>ir jobs and dropping out of <strong>the</strong>ir career fields(Cortina, Magley et al., 2001). Sexual harassmentcan affect mental and physical well-being throughincreased stress, anxiety, and depression and loweredself-esteem (Bowling & Beehr, 2006). Theseeffects extend beyond <strong>the</strong> employees targeted byharassers. Female and male employees who witnessgender-based hostility at work also express greaterorganizational withdrawal and lower well-being(Miner-Rubino & Cortina, 2007).Not all women are equally vulnerable to sexualharassment. Women of color are more likely toexperience sexual harassment as well as racial/ethnic-based harassment (Berdahl & Moore, 2006;Cortina, Kabat-Farr et al., 2013). Additionally,women in positions of authority are more likely to<strong>report</strong> harassing behaviors than are women in nonsupervisorypositions, which supports <strong>the</strong> idea thatsexual harassment may be connected to dominanceand control (McLaughlin et al., 2012; Stainbacket al., 2011; Chamberlain et al., 2008). The negativeeffects of workplace harassment are mitigatedin workplaces where women believe that <strong>the</strong>y havea strong organizational voice (Settles, Cortina,Stewart et al., 2007).
AAUW 43How structural andcultural barriersaffect womenThe factors described above have tangible effects onwomen in engineering and computing. From influencinggirls’ and women’s preferences to <strong>the</strong>ir senseof belonging in <strong>the</strong>se fields, cultural and structuralelements, including stereotypes, biases, microinequities,and sexism, shape girls’ and women’s experiencesin engineering and computing.Stereotypes inform preferencesGender biases affect not only how we view andtreat o<strong>the</strong>rs but also how we view ourselves and <strong>the</strong>choices we make about our own futures. From earlychildhood, cultural stereotypes guide our choicesand behavior, steering us toward certain careers thatseem to be <strong>the</strong> best fit for our interests and abilitiesand away from o<strong>the</strong>rs. Studies suggest that girlswho associate ma<strong>the</strong>matics with boys and men areless likely to perceive <strong>the</strong>mselves as being interestedin or skilled at ma<strong>the</strong>matics and spend less timestudying or engaging with ma<strong>the</strong>matics concepts.As early as first grade, children have alreadydeveloped a sense of gender identity, and most havedeveloped implicit biases associating boys withmath as well (Cvencek, Meltzoff et al., 2011).As described in chapter 4, individuals’ implicitbiases are related to <strong>the</strong>ir college majors, withwomen in science and engineering exhibitingparticularly weak, and men in those fields exhibitingparticularly strong, science-male implicit biases(Smyth, Greenwald et al., 2015; Nosek & Smyth,2011; Lane et al., 2012; Smeding, 2012). Although<strong>the</strong> causal direction is not known, researchers suspectthat implicit biases likely influence <strong>the</strong> choicesthat women and men make, while at <strong>the</strong> same time<strong>the</strong> environments in which women and men areimmersed shape <strong>the</strong>ir implicit biases.Jacquelynne Eccles, a leading researcher in <strong>the</strong>field of occupational choice, has spent <strong>the</strong> past 35years developing a model and collecting evidenceabout career choice (Eccles [Parsons] et al., 1983;Eccles, 1994, 2007). She found that women areless likely than men to enter occupations such asengineering and computing because <strong>the</strong>y have lessconfidence in <strong>the</strong>ir math and physical science abilitiesand because <strong>the</strong>y place less subjective value on<strong>the</strong>se fields than <strong>the</strong>y place on o<strong>the</strong>r occupationalniches (Eccles, 2011b).Many researchers have found a perceived differencein <strong>the</strong> value that women and men placeon doing work that contributes to society, withwomen, on average, more likely than men to preferwork with a clear social purpose ( Jozefowicz et al.,1993; Margolis, Fisher, & Miller, 2002; Lubinski& Benbow, 2006; Eccles, 2007). A meta-analysisof job-attribute preferences found that <strong>the</strong> largestgender differences in desired job characteristics arerelated to communal goals, that is, helping o<strong>the</strong>rpeople and working with people, with womenexpressing a greater preference for both (Konradet al., 2000). As described in chapter 6, engineeringand computing careers are perceived by mostpeople as inhibiting communal goals, and individualswho highly endorse communal goals are lesslikely to express interest in <strong>the</strong>se fields (Diekman,Brown et al., 2010).If women perceive engineering and computingas fields that will not allow <strong>the</strong>m to meet highlyvalued goals, it is not surprising that <strong>the</strong>y mightchoose o<strong>the</strong>r career paths, even o<strong>the</strong>r STEMcareer paths (Benbow, 2012). Eccles and her colleaguesfound that <strong>the</strong> desire at age 20 to havea job that helps people is a very strong predictorof both women and men completing a major in<strong>the</strong> biological ra<strong>the</strong>r than <strong>the</strong> physical sciences ormath and working in biological or medical occupationsra<strong>the</strong>r than physical science or engineeringoccupations at age 25 (Eccles, 2009, 2011a, as<strong>report</strong>ed in Kimmell et al., 2012). In <strong>the</strong> same vein,Harrison and Klotz (2010) found that <strong>the</strong> percentageof women in sustainability leader positions indesign and construction companies, a position thatexplicitly connects engineers’ contributions to problemssuch as energy and water resource depletion,climate change, and social inequity, is much higher(39 percent) than <strong>the</strong> percentage of women ingeneral management positions (8 percent) in thosesame companies.Eccles (2011b) points out that women (andmen) likely do not consider <strong>the</strong> full range of
44SOLVING THE EQUATIONoptions when choosing a career. Many options maynever be considered because women are unawareof <strong>the</strong>ir existence. For example, Google (2014b)identified exposure to computing as a leading factorin women’s choice to pursue computing. Evenwhen girls and women are aware of career options,<strong>the</strong>y may not seriously consider those optionsbecause women have inaccurate informationregarding ei<strong>the</strong>r <strong>the</strong> option itself or <strong>the</strong>ir ability toachieve in that field. For example, Teague (2002)found that <strong>the</strong> issues that deter many women frompursuing computing occupations are not supportedby <strong>the</strong> actual experiences of <strong>the</strong> women working<strong>the</strong>re. Women may not seriously consider o<strong>the</strong>rcareers because <strong>the</strong>se options do not fit well with<strong>the</strong>ir ideas of what is appropriate work for women,fur<strong>the</strong>r reducing women’s perceptions of <strong>the</strong> field ofviable options.Focusing on girls’ and women’s choices mightseem to “blame <strong>the</strong> victim”—women—for <strong>the</strong>irunderrepresentation in engineering and computing.According to sociologist Maria Charles (2011,p. 25), however, acknowledging gender differencesin educational and career choices doesn’tblame women for women’s underrepresentationin engineering and computing unless preferencesand choices are understood purely as a reflectionof individuals’ intrinsic qualities, separate from <strong>the</strong>social environment in which preferences emerge:The argument that women’s preferencesand choices are partly responsible for sexsegregation doesn’t require that preferencesare innate. Career aspirations are influencedby beliefs about ourselves (what am I goodat and what will I enjoy doing?), beliefsabout o<strong>the</strong>rs (what will <strong>the</strong>y think of meand how will <strong>the</strong>y respond to my choices?),and beliefs about <strong>the</strong> purpose of educationaland occupational activities (how do I decidewhat field to pursue?). And <strong>the</strong>se beliefs arepart of our cultural heritage. Sex segregationis an especially resilient form of inequalitybecause people so ardently believe in, enact,and celebrate cultural stereotypes aboutgender difference.Recent analyses of international differencesin <strong>the</strong> composition of engineering and computingfields make clear that <strong>the</strong> surroundingculture makes a difference (Frehill & Cohoon,2015; Charles, 2011). While in <strong>the</strong> United Statesapproximately one-fifth of computer sciencedegrees are awarded to women, in Malaysia womenearn about half of computer science degrees.Similarly, engineering is <strong>the</strong> most strongly andconsistently male-typed field of study worldwide,but <strong>the</strong> gender composition of engineering varieswidely across countries. In <strong>the</strong> United States fewerthan one-fifth of engineering degrees are awardedto women, but in Indonesia women earn just underhalf of engineering degrees. Women make up abouta third of recent engineering graduates in a diversegroup of countries, including Mongolia, Greece,Serbia, Panama, Denmark, Bulgaria, and Malaysia(Charles, 2011).In <strong>the</strong> United States and o<strong>the</strong>r industrializedcountries, individuals and especially girls areencouraged to choose careers based on self-expressionand self-realization, whereas in developingcountries personal economic security and nationaldevelopment are often much more central concernsto young people and <strong>the</strong>ir parents. Perhaps ironically,this allows women in countries such as <strong>the</strong>United States more opportunity to conform togender stereotypes in <strong>the</strong>ir career choices (Charles& Bradley, 2009; England, 2010).Stereotype threatIn addition to affecting preferences, stereotypesaffect women through a phenomenon known asstereotype threat. Stereotype threat describes athreat—sometimes referred to as an anxiety—thatpeople experience when <strong>the</strong>y fear being judged interms of a group-based stereotype (Steele, 1997;Steele & Aronson, 1995). One need not believe<strong>the</strong> stereotype nor be worried that it is true ofoneself to experience stereotype threat and itsnegative effects. To be susceptible, individuals mustonly be aware of <strong>the</strong> stereotype, identify with <strong>the</strong>group that is stereotyped, and care about succeedingin <strong>the</strong> domain in which <strong>the</strong> stereotype applies
AAUW 45(Steele, 1997). For this last reason, people whocare <strong>the</strong> most about succeeding in a domain mayexperience <strong>the</strong> highest levels of stereotype threat.Robust gender-math stereotypes in U.S. culturemake stereotype threat an important phenomenonin understanding women’s underrepresentation inengineering and computing.Stereotype threat has many negative effects,including physiological stress responses such asa faster heart rate, increased cortisol levels, andincreased skin conductance related to increasedmonitoring of one’s performance and efforts toregulate unwanted negative thoughts and feelings.These extra processes are understood to “hijackcognitive resources” (Schmader & Croft, 2011,p. 792)—specifically working memory capacity—neededfor successful performance (Schmader,2010; Schmader, Forbes et al., 2009; Schmader,Johns et al., 2008; Schmader & Johns, 2003).Stereotype threat has been shown to result indecreased math performance among women (Kochet al., 2014; Spencer et al., 1999; Nguyen & Ryan,2008; Walton & Spencer, 2009; Good, C., Aronsonet al., 2008), decreased interest and motivation inSTEM fields among women (Davies et al., 2002),and decreased sense of belonging (Walton &Cohen, 2007). It ultimately may result in disidentificationwith <strong>the</strong> stereotyped domain (Steele,Spencer et al., 2002; Steele, 1997). Stereotypethreat can be particularly harmful to women ofcolor because <strong>the</strong>y have to contend with <strong>the</strong> threatof confirming stereotypes based on both race andgender (Settles, 2004).Stereotype threat is triggered by cues from <strong>the</strong>environment that alert an individual to <strong>the</strong> possibilityof confirming a negative stereotype about agroup to which she or he belongs. Cues are oftenquite subtle. For example, being a member of aminority group, as women in engineering andcomputing often are, in and of itself can trigger asense of threat. In one study female STEM majorswho viewed a video of a scientific conference withnoticeably more men than women in attendanceexhibited higher indications of stereotype threatand <strong>report</strong>ed a lower sense of belonging and lessdesire to participate in <strong>the</strong> conference comparedwith women who viewed a similar video with agender-balanced group of attendees (Murphy et al.,2007). In ano<strong>the</strong>r experiment, subtle sexist behaviorby men triggered stereotype threat in female engineeringmajors resulting in <strong>the</strong>ir underperformanceon a math, but not a verbal, test (Logel et al., 2009).Ano<strong>the</strong>r study found that when watching avideo in which a woman was subjected to dominantbehavior, including command statements like “youneed to...” combined with gesturing and a relaxedposture by a man in a math context, female participantsshowed reduced math performance and<strong>report</strong>ed greater worry about confirming <strong>the</strong> negativestereotype that women are not as good as menat math. When women watched a video in which<strong>the</strong> man and woman were equal in dominance or<strong>the</strong> woman was dominant over <strong>the</strong> man, however,female participants did not experience stereotypethreat (Van Loo & Rydell, 2014). This last findingdemonstrates <strong>the</strong> potentially far-reaching benefitsof encouraging equality and female leadershipin <strong>the</strong> classroom and workplace, because seeingwomen in leadership roles can actually protecto<strong>the</strong>r women from <strong>the</strong> harmful effects of stereotypethreat.Until recently, research on stereotype threatfocused primarily on <strong>the</strong> effect of stereotype threaton academic performance in <strong>the</strong> learning environment.Researchers are just beginning to explore<strong>the</strong> effects of stereotype threat in <strong>the</strong> workplace,focusing less on performance measures and moreon measures of psychological disengagement, suchas <strong>the</strong> degree to which women and men might say<strong>the</strong>y feel disconnected from <strong>the</strong>ir work or mentallyexhausted at <strong>the</strong> end of <strong>the</strong> day. A study describedin chapter 5 found that <strong>the</strong> more female sciencefaculty members discussed research with male colleagues,<strong>the</strong> more disengaged women felt from <strong>the</strong>irwork. The more women socialized with <strong>the</strong>ir malecolleagues, on <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> more engagedwomen felt with <strong>the</strong>ir work (Holleran et al., 2011).The researchers hypo<strong>the</strong>size that research conversationswith male colleagues may trigger stereotypethreat among female scientists, whereas social conversationsmay increase feelings of belonging and,<strong>the</strong>refore, reduce experiences of stereotype threat.
46SOLVING THE EQUATIONBecause <strong>the</strong> effect of stereotype threat in <strong>the</strong> learningenvironment has been so clearly and repeatedlydemonstrated, it is evident that stereotypescan affect stereotyped individuals in importantways. Understanding how stereotype threat affectswomen in <strong>the</strong> workplace, especially in fields suchas engineering and computing, is an important areafor future research.Sense of belongingPerhaps because of all <strong>the</strong>se factors taken toge<strong>the</strong>r,women often <strong>report</strong> feeling that <strong>the</strong>y don’t belongin engineering and computing fields (Ayre et al.,2013; Faulkner, 2009b). Research described inchapter 8 shows that even among first-year engineeringstudents, women are less likely to perceiveengineering as <strong>the</strong> right career for <strong>the</strong>m (Cech,Rubineau et al., 2011).A sense of belonging in a particular setting orbroader field is associated with a variety of positiveoutcomes for individuals (Walton, Cohen et al.,2012; Walton & Cohen, 2007). For example, a briefintervention aimed at increasing first-year collegestudents’ sense of social belonging was foundto positively affect participants’ GPA and self<strong>report</strong>edhealth and well-being (Walton & Cohen,2011). Even more relevant, women participants ina social-belonging intervention who learned thatadversities and worries about belonging were commonfor all engineering students raised <strong>the</strong>ir engineeringGPAs, improved <strong>the</strong>ir academic attitudes,and viewed <strong>the</strong>ir daily adversities as more manageable(Walton, Logel et al., 2014). In ano<strong>the</strong>r study,women showed improved scores on math testsif <strong>the</strong>y wrote a brief essay about social belongingbeforehand (Shnabel et al., 2013). Finally, a seriesof lab studies found that sense of belonging inmath is a good predictor of whe<strong>the</strong>r women willcontinue to take math courses (Good, C., Rattan etal., 2012). Sense of belonging can have importanteffects even when individuals are unconscious of it.In some of <strong>the</strong> above studies, participants indicatedno awareness of <strong>the</strong> intervention’s impact (Walton& Cohen, 2011).Where do we go from here?The chapters that follow examine specific researchfindings on pivotal issues affecting <strong>the</strong> representationof women in computing and engineering. Theresults suggest that with small and large changes ineducation and <strong>the</strong> workplace, progress can be madefor <strong>the</strong> existing generation of women in <strong>the</strong>se fieldsas well as future generations.
Chapter 3.Gender Bias andEvaluationsWhen I hear <strong>the</strong> idea that women are “choosing” notto study science, technology, engineering, and math,I think that might be true, but <strong>the</strong>re might be goodreasons <strong>the</strong>y’re choosing not to study <strong>the</strong>se subjects,and one of those reasons could be discrimination.—Ernesto Reuben
48SOLVING THE EQUATIONDespite our best intentions, most of us evaluateand treat women differently than we do men.Evidence shows that bias against women, particularlyin stereotypically male domains, is widespread(Banaji & Greenwald, 2013; Heilman, Wallen etal., 2004). This chapter highlights two studies thatdemonstrate <strong>the</strong> prevalence and real-world impactof gender bias in hiring decisions. The first studyexamines <strong>the</strong> impact of gender bias on evaluationsby science faculty, and <strong>the</strong> second study explores<strong>the</strong> effect of implicit gender bias (bias that operatesautomatically, typically outside an individual’s consciousawareness) on evaluations in <strong>the</strong> populationmore broadly. Both studies make clear that genderbias affects people’s evaluations of one ano<strong>the</strong>r and,specifically, hiring decisions.A study of gender biasesamong scientistsScientists by definition strive to be objective. Ifanyone were immune to <strong>the</strong> effects of gender bias, ascientist would be a likely candidate. Academic scientistsare an especially important group given <strong>the</strong>irinfluence on <strong>the</strong> students who will become <strong>the</strong> nextgeneration of scientists, engineers, and computingprofessionals.Corinne Moss-Racusin, an assistant professorat Skidmore College, and her former colleagues atYale University, John Dovidio, Victoria Brescoll,Mark Graham, and Jo Handelsman, examinedwhe<strong>the</strong>r faculty members in biology, chemistry,and physics departments at three public and threeprivate large research universities across <strong>the</strong> countrymake gender-biased judgments. Keeping <strong>the</strong> truepurpose of <strong>the</strong> study hidden, <strong>the</strong> researchers contactedfaculty members asking for help in developingappropriate mentoring programs for undergraduatescience students. As part of <strong>the</strong> study,each scientist who agreed to participate was askedto provide feedback on an application for a studentscience-laboratory manager position. Half of <strong>the</strong>science professors reviewed an application froma student named “Jennifer,” while <strong>the</strong> o<strong>the</strong>r halfreviewed an identical application from a studentnamed “John.” Moss-Racusin told AAUW:We pre-tested <strong>the</strong> names and had <strong>the</strong>mrated on trait dimensions, like how intelligent<strong>the</strong>y sounded, how recognizable, howwarm, how likeable, etc. We chose <strong>the</strong> namesthat were rated as equivalent, so any differencesbetween <strong>the</strong> two conditions shouldbe attributable solely to <strong>the</strong> gender of <strong>the</strong>student and not to any superficial differencesbetween <strong>the</strong> names.The applicants’ credentials were purposelypresented to describe someone who could quitepossibly be successful as a lab manager but was notan obvious star—a “qualified but not irrefutablyexcellent applicant” (Moss-Racusin, Dovidio et al.,2012b, p. 2). This approach was chosen to replicate<strong>the</strong> situation for most aspiring scientists, including<strong>the</strong> type of students most affected by faculty judgmentsand mentoring. The science faculty members(33 women and 94 men) 1 assessed <strong>the</strong> applicanton competence, hirability, and likeability and told<strong>the</strong> researchers <strong>the</strong> salary and mentoring that <strong>the</strong>ywould offer <strong>the</strong> student, with <strong>the</strong> understandingthat <strong>the</strong>ir feedback would be shared with <strong>the</strong>student <strong>the</strong>y had rated (Moss-Racusin, Dovidio etal., 2012b).Pervasive gender discriminationamong science facultyThe results were unequivocal. Scientists, bothwomen and men, viewed <strong>the</strong> female applicant asless competent and less hirable than <strong>the</strong> identicalmale applicant and were less willing to mentor<strong>the</strong> female candidate than <strong>the</strong> male candidate (seefigure 14). Faculty members also indicated that<strong>the</strong>y would offer less money to <strong>the</strong> woman than<strong>the</strong> man. Asked to choose a starting salary rangingfrom $15,000 per year to $50,000 per year, scientistsindicated that <strong>the</strong>y would offer an average ofslightly more than $26,500 to <strong>the</strong> female applicantand an average of slightly more than $30,000 to <strong>the</strong>male applicant.Although scientists often <strong>report</strong>ed liking <strong>the</strong>female student more than <strong>the</strong> male student, likingher more did not translate into positive perceptionsof her competence or into material outcomes suchas a job offer, good salary, or career mentoring.
AAUW 49FIGURE 14. FACULTY RATINGS OF LAB MANAGER APPLICANT, BY GENDER OF APPLICANT7WomanMan6Average rating (scale from 1 to 7)543210CompetenceHirabilityMentoringRATING CATEGORIESSource: Moss-Racusin, Dovidio et al. (2012a).The researchers also looked into <strong>the</strong> underlyingfactors that might influence this behavior andfound that once <strong>the</strong>y controlled for <strong>the</strong> perceivedcompetence of <strong>the</strong> applicant, scientists’ preferencefor hiring men disappeared. This finding indicatesthat scientists were less likely to hire a woman thanan identical man because <strong>the</strong>y viewed <strong>the</strong> woman asless competent, not because of some o<strong>the</strong>r potentialreason (such as a perception that a woman wouldbe less committed to <strong>the</strong> job or more likely to leave<strong>the</strong> position).This study provides unique experimental evidencethat science faculty members discriminateagainst female undergraduates. Female and malescientists were equally likely to discriminate againstfemale applicants, and scientific field, age, andtenure status had no effect on <strong>the</strong> results. Facultyin all scientific fields discriminated against women,women were as likely as men to discriminate, andyounger faculty were as likely as older faculty todiscriminate.Subtle gender bias linkedto discriminationAfter providing feedback on <strong>the</strong> male or femaleapplicant, <strong>the</strong> scientists completed <strong>the</strong> ModernSexism Scale, a commonly used and well-validatedscale that measures modern views toward womenand gender (Swim et al., 1995). This scale isdesigned to detect explicit gender bias focusing onsubtle, contemporary manifestations of bias ra<strong>the</strong>rthan more old-fashioned, overtly hostile attitudestoward women. For example, participants rated<strong>the</strong>ir agreement with statements such as “On average,people in our society treat husbands and wivesequally” and “Discrimination against women is nolonger a problem in <strong>the</strong> United States.”The researchers found that faculty memberswith subtle gender biases made more negativeevaluations of female applicants. That is, <strong>the</strong> moregender bias <strong>the</strong> scientists expressed on <strong>the</strong> ModernSexism Scale, <strong>the</strong> less competent and hirable <strong>the</strong>yperceived <strong>the</strong> female applicant to be and <strong>the</strong> less
50SOLVING THE EQUATIONmentoring <strong>the</strong>y were willing to offer her. Moss-Racusin explains:We are not talking about faculty membersrefusing to meet with a female student underany circumstances or telling a female studentsomething egregious like “You don’t belonghere, and I would never train you.” Theiroutcomes or behaviors are just slight downgradesof what <strong>the</strong>y would offer <strong>the</strong> identicalmale student—<strong>the</strong>y’re tweaks. We arenot talking about a $30,000 differential i<strong>nsa</strong>lary; we are talking about $4,000 per year.… Yet, <strong>the</strong>se subtle downgrades accumulateover time and may come to powerfully shapechoices and careers.How bias affects women inengineering and computingAccording to Moss-Racusin, <strong>the</strong> findings of thisexperiment are relevant to <strong>the</strong> engineering andcomputing fields because <strong>the</strong> biased evaluationsfound here are driven by stereotypes that womenare not well suited to science. Gender stereotypesmay be even stronger in <strong>the</strong> engineering andcomputing fields, where women are even less wellrepresented than in <strong>the</strong> scientific fields surveyed inthis study.The differences uncovered in this study couldtranslate into large, real-world disadvantages forwomen in engineering and computing. Becauseconfidence develops, in part, from feedback fromour environment (Bandura, 1982), teacher and facultyassessments of students’ competence contributeto students’ career choices. This study suggests thatgirls and young women are less likely than <strong>the</strong>irmale peers to receive good evaluations of <strong>the</strong>ircompetence, rewards, and mentoring from <strong>the</strong>irteachers and professors throughout <strong>the</strong>ir STEMeducation, even when <strong>the</strong>ir performance is <strong>the</strong>same as boys’ and men’s.Biases continue to affect women once <strong>the</strong>ymove into <strong>the</strong> workplace. From hiring decisionsto project assignments to promotions, <strong>the</strong> findingsfrom this study reveal that female engineers andcomputing professionals are likely to be evaluatedas less competent, less hirable, and less valuablethan identically qualified male counterparts.Moss-Racusin and her colleagues’ study exposes<strong>the</strong> lack of a level playing field in academic science.One might think that science professors, by virtueof <strong>the</strong>ir rigorous training in producing objectiveoutcomes, would not be influenced by genderbiases, but this study demonstrates that this issimply not true. Ironically, a belief in one’s objectivitymay increase biased behavior (Uhlmann &Cohen, 2007). Moss-Racusin told AAUW, “We areall—even those of us who are extremely focused onbeing egalitarian—exposed to <strong>the</strong>se pervasive stereotypesthroughout our surrounding culture, andwe’re all fairly affected by <strong>the</strong>m.” Understandingthat bias is widespread is an important first steptoward reducing biased behavior.The biases uncovered in Moss-Racusin and hercolleagues’ study are likely a combination of explicitbiases and implicit biases. The study found anassociation between stated subtle gender bias andlower assessment of women’s competence, suggestingan important effect of explicit bias. At <strong>the</strong> sametime, gender bias emerged as a general effect in <strong>the</strong>whole sample, including <strong>the</strong> scientists who did notexplicitly express even subtle gender biases, suggestingan important effect of implicit bias operatingmostly beneath <strong>the</strong> level of consciousness. 2Implicit gender biases anddiscrimination in hiringSome years ago Ernesto Reuben, an economist atColumbia Business School, noticed a number ofarticles on gender differences in personal characteristicssuch as risk-taking, willingness to negotiate,and enjoyment of competition. These articles proposedthat women’s preferences related to factorssuch as <strong>the</strong>se explained women’s underrepresentationin certain positions and fields. Reuben toldAAUW, “After reading <strong>the</strong>se articles, I thought<strong>the</strong>y were missing something. I thought that weneed to be thinking not only about how womenare different from men but about how perceptionscloud judgment and how this can filter back intowomen’s incentives to even try for certain types ofjobs.”Reuben wondered if something was going onin <strong>the</strong> hiring environment that might help explain
AAUW 51women’s underrepresentation in certain positio<strong>nsa</strong>nd fields. To explore this question, he and his colleagues,Paola Sapienza from <strong>the</strong> Kellogg Schoolof Management at Northwestern University andLuigi Zingales from <strong>the</strong> Booth School of Businessat <strong>the</strong> University of Chicago, conducted a studyon <strong>the</strong> practical effects of implicit gender biases onhiring practices (Reuben et al., 2014a, 2014b).A hiring experimentIn a laboratory experiment focused on hiring, justunder 200 undergraduate students—in groups ofaround 14 students each—performed an arithmetictask on a computer, summing as many sets of fourtwo-digit numbers as possible during a four-minuteperiod, and <strong>the</strong>n took part in a hiring exercise.Participants were told that <strong>the</strong>y would be paid asmall amount according to <strong>the</strong> number of correctanswers <strong>the</strong>y provided and additional money if <strong>the</strong>ywere chosen to be hired. Payment was offered as away to motivate participants to think hard about<strong>the</strong> questions and to want to be hired, as wouldmost often be <strong>the</strong> case in an actual hiring scenario.The researchers chose <strong>the</strong> arithmetic task becauseresearch shows that it is performed equally well bywomen and men while at <strong>the</strong> same time belongingto an area (ma<strong>the</strong>matics) about which <strong>the</strong>reremains a pervasive stereotype that men performbetter.Following <strong>the</strong> arithmetic task, participantswere told <strong>the</strong> number of problems <strong>the</strong>y had solvedcorrectly. The participants also were told that <strong>the</strong>ywould perform <strong>the</strong> same task a second time andwere asked to estimate how many questions <strong>the</strong>ywould answer correctly <strong>the</strong> second time. They alsowere told that <strong>the</strong>ir earnings, which were againbased on <strong>the</strong> number of correct answers <strong>the</strong>yprovided, would not be affected by <strong>the</strong> accuracy of<strong>the</strong>ir estimate.Before <strong>the</strong> second task began two participantswere chosen randomly to be “job candidates.”They walked to <strong>the</strong> front of <strong>the</strong> room carrying“Candidate A” and “Candidate B” signs, while <strong>the</strong>remaining participants acted as “employers,” taskedwith hiring one of <strong>the</strong> two candidates to perform<strong>the</strong> second arithmetic task. If employers chosecorrectly—that is, actually chose <strong>the</strong> candidate whoperformed better than <strong>the</strong> o<strong>the</strong>r candidate on <strong>the</strong>second arithmetic task—<strong>the</strong>y received increasedcompe<strong>nsa</strong>tion for <strong>the</strong> study. 3 After <strong>the</strong> employersmade <strong>the</strong>ir hiring decision, <strong>the</strong> experiment wasrepeated a number of times with o<strong>the</strong>r randomlychosen job candidates. Employers chose candidatesfrom pairs representing any combination of womenand men including same-sex pairs to avoid makinggender overly obvious as a factor in employers’ decisions;however, <strong>the</strong> researchers analyzed data onlyfrom <strong>the</strong> instances in which <strong>the</strong> two candidates in<strong>the</strong> pair were of different genders.Phase 1: Hiring decisions based on <strong>the</strong>candidates’ appearance onlyThe evaluation portion of <strong>the</strong> study included threephases. In phase 1, employers were asked to choosebetween <strong>the</strong> two candidates based only on <strong>the</strong>applicants’ appearance. As might be expected, whenbasing a hiring decision purely on appearance,employers frequently made bad hiring decisions,selecting <strong>the</strong> higher-performing candidate onlyabout half (55 percent) of <strong>the</strong> time (see figure 15).Not surprisingly, <strong>the</strong> bad hiring decisions werenot gender neutral. Employers were more thantwice as likely to choose <strong>the</strong> man as <strong>the</strong> womanwhen <strong>the</strong>y made a bad hiring decision, choosing<strong>the</strong> lower-performing man 32 percent of <strong>the</strong> timeand <strong>the</strong> lower-performing woman 14 percent of<strong>the</strong> time. Both female and male employers expectedwomen to perform worse than men.Phase 2: Hiring decisions based on <strong>the</strong>candidates’ predictions of futureperformanceIn phase 2, employers were shown <strong>the</strong> candidates’estimates of <strong>the</strong>ir performance on <strong>the</strong> secondarithmetic task. This phase simulated a real-lifeinterview situation in which employers ask—andoften have to take a candidate’s word for—howcandidates would expect to perform in a given job,a scenario especially common in situations wherejob skills are less quantifiable and more subjective.With this limited additional information, <strong>the</strong>employers in <strong>the</strong> study did a better job, choosing<strong>the</strong> top performer 69 percent of <strong>the</strong> time (seefigure 15). Yet in this scenario, when employers
52SOLVING THE EQUATIONFIGURE 15. PROBABILITY OF SELECTING THE BESTCANDIDATE FOR A MATHEMATICAL TASKPercentage10080604020014%32%55%Appearance onlyLower-performingwoman29%69%Candidate'sexpressedanticipatedperformanceLower-performingmanObjectivepast performanceINFORMATION PROVIDED TO EMPLOYERHigher-performingcandidate(woman or man)Notes: The percentage of times that a lower-performing man was chosen in eachcondition was calculated from information provided in figure 1 in <strong>the</strong> source. Theauthors multiplied <strong>the</strong> percentage of times <strong>the</strong> lower-performing candidate was chosenby <strong>the</strong> percentage of times <strong>the</strong> lower-performing chosen candidate was a man.Source: Reuben et al. (2014a).2%7%12%81%knew <strong>the</strong> candidates’ estimated future performance,employers still chose a lower-performing man(over a higher-performing woman) 29 percentof <strong>the</strong> time—nearly as often as when employersmade <strong>the</strong>ir decision based solely on appearance.In contrast, employers chose a lower-performingwoman only 2 percent of <strong>the</strong> time. In o<strong>the</strong>r words,employers made fewer mistakes overall when <strong>the</strong>yknew candidates’ estimates of <strong>the</strong>ir future performance,but when employers did make a bad hiringdecision, <strong>the</strong>y nearly always erred in favor of <strong>the</strong>lower-performing man and almost never in favor of<strong>the</strong> lower-performing woman.Phase 3: Hiring decisions based onobjective past performanceIn phase 3, employers were told <strong>the</strong> actual performanceof each candidate on <strong>the</strong> first task. In thiscase, employers did an even better job, choosing <strong>the</strong>top performer 81 percent of <strong>the</strong> time (see figure15). When employers hired <strong>the</strong> lower-performingcandidate, <strong>the</strong>y were still nearly twice as likely tohire <strong>the</strong> lower-performing man over <strong>the</strong> higherperformingwoman than <strong>the</strong> reverse, but <strong>the</strong> genderdiscrepancy was not as great as when employersbased <strong>the</strong>ir decisions on candidates’ appearance orcandidates’ future anticipated performance.Implicit biases associated withdiscriminatory behaviorThe researchers also asked all participants to completea math/science-gender Implicit AssociationTest (Greenwald, McGhee et al., 1998). The testresults were clear: Implicit gender biases wererelated to discriminatory hiring practices. The studyfound a strong correlation between implicit math/science-male bias and bad hiring decisions favoringmen.Employers with stronger math/science-malebiases were more likely than o<strong>the</strong>rs to choose aman ra<strong>the</strong>r than a woman in each phase of <strong>the</strong>study. The study found that employers, especiallythose with stronger math/science-male implicitbiases, are likely to rely on <strong>the</strong>ir own stereotypesand biases when hiring employees, even whenobjective past performance information is available.Frequently, <strong>the</strong>se biases will lead employers to hire<strong>the</strong> less capable candidate.Implications for women in engineeringand computingThis study presents a discouraging scenario forwomen in typically male fields such as engineeringand computing. No matter what type of informationemployers had about applicants, employers’ badhiring decisions usually favored a low-performingmale candidate over a high-performing femalecandidate.
AAUW 53In both <strong>the</strong> first and second phases of <strong>the</strong> study,nearly one in three times <strong>the</strong> higher-performingwoman did not get hired. The odds of higher-performingmen getting hired were much better thanhigher-performing women getting hired in everyscenario, especially when employers based <strong>the</strong>irdecisions on candidates’ predictions of <strong>the</strong>ir futureperformance.One might think that this study overestimates<strong>the</strong> effect of biases on hiring because employersundoubtedly have experience with hiring thatshould give <strong>the</strong>m an edge in accurately evaluatingcandidates over <strong>the</strong> student employers in <strong>the</strong> experiment.Real-world employers also have much moreat stake and, <strong>the</strong>refore, should be more inclined toseek information about past performance (such asreferences). While true, at <strong>the</strong> same time, this studymay underestimate <strong>the</strong> effect of biases on hiring,since past performance is likely to be even moreambiguous in reality than in this experiment, andstereotypes thrive in cases where qualifications areambiguous. Reuben told AAUW that he believes<strong>the</strong> findings from this study are quite relevant toreal-world situations:These implicit associations that we aremeasuring or capturing—<strong>the</strong>se are not from<strong>the</strong> lab. They were not created in <strong>the</strong> lab. Weare just putting study participants in a contextwhere <strong>the</strong>y have to make choices, and<strong>the</strong>y do so based on <strong>the</strong>ir biases. I don’t seewhy <strong>the</strong> biased decisions we see in <strong>the</strong> labwouldn’t translate to real decisions outside.The advantage of objectiveinformationThis study provides clues about reducing <strong>the</strong>effect of gender bias on hiring. The more objectiveinformation employers had to go on, <strong>the</strong> better andless-biased decisions <strong>the</strong>y made. Specifically, whenemployers had information about applicants’ pastmath performance, <strong>the</strong>y chose <strong>the</strong> top-performingcandidate 81 percent of <strong>the</strong> time. In contrast,employers chose <strong>the</strong> top performer only 55 percentand 69 percent in phases one and two of <strong>the</strong>study, when <strong>the</strong>y had less-objective performanceinformation on which to base <strong>the</strong>ir decisions. Theresearchers found that self-<strong>report</strong>ed informationdoesn’t help as much as objective informationbecause “men tend to be more self-promotingthan women in <strong>the</strong>se <strong>report</strong>s but employers … donot fully appreciate <strong>the</strong> extent of this difference”(Reuben et al., 2014a, p. 4408).While objective information helps, it doesn’tfully overcome <strong>the</strong> problem of gender-biased hiring.Even when participant employers had objectiveinformation on <strong>the</strong> past performance of candidates,<strong>the</strong>y chose to hire <strong>the</strong> lower performer one in everyfive times, and when <strong>the</strong>y did, <strong>the</strong>y were twice aslikely to hire <strong>the</strong> lower-performing man as <strong>the</strong>lower-performing woman. This gender gap in hiringdecisions is due to a systematic underestimationof <strong>the</strong> performance of women compared with men(Reuben et al., 2014b). While it can be difficult tolearn objective information about a job applicant’spast performance that is relevant for <strong>the</strong> job she orhe is seeking, <strong>the</strong> findings from this study suggestthat <strong>the</strong> more employers can base <strong>the</strong>ir hiringdecisions on objective information, <strong>the</strong> better—andless-biased—decisions <strong>the</strong>y will make.What can we do?Chapter 10 includes recommendations, basedon <strong>the</strong> large body of gender-bias literature, forcounteracting <strong>the</strong> harmful effects of gender bias.From <strong>the</strong>se two studies, several recommendatio<strong>nsa</strong>re clear.A first step toward addressing biases in hiring isacknowledging <strong>the</strong> reality that we are all influencedby gender biases, whe<strong>the</strong>r or not we consciouslyendorse <strong>the</strong>m. Reuben told AAUW that, for <strong>the</strong>most part, “we are not trying to convince peoplethat discrimination is bad anymore. Now we’re justtrying to make people aware that <strong>the</strong>y might bediscriminating.” Requiring researchers who receivefederal funds to participate in training on how tocounteract <strong>the</strong> harmful effects of bias could helpincrease awareness. One practical way to do thisis to incorporate bias training into <strong>the</strong> responsibleconduct of research (RCR) training alreadyrequired for individuals, including many academic
54SOLVING THE EQUATIONscientists, who work on federally funded researchprojects. Institutions should consider requiring biastraining for all administrators as well.Second, Reuben and his colleagues found thatemployers made better and less-biased hiringdecisions when <strong>the</strong>y were provided with objectiveinformation about past performance. Employersshould base hiring decisions on objective past performanceinformation as much as possible.Third, more research is needed to establish<strong>the</strong> effectiveness of interventions aimed at reducingboth implicit and explicit bias, particularlyresearch in applied settings including workplacesthat employ engineers and computing professionals.Moss-Racusin and her colleagues recentlypublished recommendations for <strong>the</strong> design andevaluation of bias-reduction interventions to ensurethat <strong>the</strong>y are evidence based and scientifically validated(Moss-Racusin, van der Toorn et al., 2014).She told AAUW:I hope that in 10 years we will have some<strong>the</strong>ory-driven, solid, validated programs forprejudice reduction and boosting diversity inSTEM. I hope that we will be able to showthat if we administer interventions under<strong>the</strong>se circumstances to <strong>the</strong>se folks, we aremost likely to see improvement. I hope thatwe will have a deep tool kit, that we willknow, “for this kind of audience this wouldbe best; for that kind of audience that wouldbe best.” I don’t think we are <strong>the</strong>re yet, butwe are moving <strong>the</strong>re.Chapter 3 notes1. The demographic breakdown of <strong>the</strong> study’s sample is consistent with <strong>the</strong> demographic breakdown of <strong>the</strong> population of scienceprofessors in <strong>the</strong> United States overall. Because of this, <strong>the</strong> results of this study can be generalized, with some confidence, to <strong>the</strong>broader population of science professors.2. Although in <strong>the</strong>ir study Moss-Racusin and her colleagues did not directly assess <strong>the</strong> science faculty members’ implicit genderbiases (for example, by administering an Implicit Association Test) for feasibility reasons, she told AAUW, “It would be veryinteresting to see if implicit gender bias is linked to effects in <strong>the</strong> same way that <strong>the</strong> modern sexism scale is.” Determining <strong>the</strong>relative impact of explicit biases compared with implicit biases on evaluations is an important question for future research.3. One advantage of this experimental design is that it narrows <strong>the</strong> factors influencing employers’ decisions to perceptions of competencealone. Extraneous factors such as employers’ concerns that women will drop out of <strong>the</strong> labor force or not be as committedto <strong>the</strong>ir jobs shouldn’t have influenced <strong>the</strong> student employers’ decisions because <strong>the</strong>y were tasked only with hiring <strong>the</strong> candidatebest able to conduct a four-minute math task within <strong>the</strong> time frame of <strong>the</strong> session.
Chapter 4.Gender Bias andSelf-ConceptsThere is a huge difference between <strong>the</strong> gender-scienceassociations in <strong>the</strong> minds of male and female scientists… and we have much to learn about how this gap mayaffect learning and work environments.—Frederick Smyth
56SOLVING THE EQUATIONImplicit biases are associated not only with howwe evaluate and treat o<strong>the</strong>rs but also with attitudesand outcomes about our own future (Smyth,Greenwald et al., 2015; Nosek, Banaji et al., 2002a).Researchers hypo<strong>the</strong>size that <strong>the</strong> influence goesin both directions: Implicit biases shape outcomesand actions (such as majoring in engineering orcomputing), and experiences (such as majoring inengineering or computing) shape implicit biases(Nosek & Smyth, 2011). Women and men areexposed to <strong>the</strong> same stereotypes about women inmath and science in U.S. culture and, on average,acquire <strong>the</strong> same implicit or unconscious “science/math=male”biases by age 7 or 8 (Cvencek,Meltzoff et al., 2011).Three long-time researchers of implicit bias,professors Brian Nosek and Frederick Smyth at<strong>the</strong> University of Virginia and professor AnthonyGreenwald at <strong>the</strong> University of Washington,examined <strong>the</strong> relationship between gender-scienceimplicit biases and engineering and computingoutcomes, specifically college major, for womenand men. The study’s sample included more than175,000 participants who visited <strong>the</strong> publicly availableProject Implicit website (implicit.harvard.edu) 1between May 2004 and January 2012. The medianparticipant age was 25, 70 percent were women,more than three-quarters were white, and all had atleast some college education.Associating sciencewith maleAt <strong>the</strong> Project Implicit website, participants indicated<strong>the</strong>ir current or past college major, answeredquestions about <strong>the</strong>ir explicit gender-science associations,and completed <strong>the</strong> gender-science ImplicitAssociation Test (IAT), which tested how rapidlyparticipants associated science with male and sciencewith female. A score of zero indicates that <strong>the</strong>participant was equally fast categorizing sciencewith male and science with female. Positive valuesindicate a stronger association (or faster categorization)of science with male than with female, 2and negative values indicate a stronger associationof science with female than with male (Smyth,Greenwald et al., 2015).Implicit science-male biasOverall, women and men both exhibited a similarstrong association of science with male. When<strong>the</strong> researchers broke down <strong>the</strong> data by collegemajor, however, striking results emerged. Men whomajored in scientific fields such as engineering orcomputing had strong implicit science-male biases,while women who majored in those fields had weakscience-male implicit biases. 3 In <strong>the</strong> least scientificfields, such as <strong>the</strong> visual and performing arts andhumanities, this situation was reversed: Womenheld much stronger science-male implicit biasesthan men did (see figure 16). Smyth told AAUW,“This big difference in implicit bias between fieldsis compelling evidence that <strong>the</strong>se implicit associatio<strong>nsa</strong>re not a one-size-fits-all. Implicit bias is notsomething that is intractable, that cannot move.”In <strong>the</strong> most scientific fields (including engineeringand computing), men held much strongerscience-male implicit biases than women did.“Those gender differences are in <strong>the</strong> realm of <strong>the</strong>biggest cognitive gender differences that we eversee,” said Smyth. Men in science and women in<strong>the</strong> humanities (those in gender-traditional fields)had <strong>the</strong> strongest biases while <strong>the</strong> weakest sciencemalebiases were found among women and men ingender-nontraditional fields.Notably, female engineers and computer scientistsdid not show a reversal in typical implicitbiases; that is, <strong>the</strong>y did not more strongly associatescience with female than with male. Smyth toldAAUW:I see it working like this: A man in sciencegets up every morning, looks in <strong>the</strong> mirror,and if he’s feeling good about his scientificachievement lately, <strong>the</strong> connection betweenmaleness and science is reinforced. Thisprocess need have nothing to do with thinkingdisparagingly about women in science.Unconsciously, routinely, this provides steadyfuel to associations between maleness and“scienceness” for him. For women, <strong>the</strong> samething happens, <strong>the</strong>ir science and femaleassociations are routinely stoked, but <strong>the</strong>ystill are in this culture where <strong>the</strong>re are somany men and still a great deal of stereotyping,so <strong>the</strong> averages for <strong>the</strong> women don’t
AAUW 57FIGURE 16. AVERAGE SCIENCE-MALE IMPLICIT ASSOCIATION TEST SCORE,BY GENDER AND COLLEGE MAJOR1.4WomenMen1.2Implicit science-male score(standard deviations from 0)1.00.80.60.40.20.0Visual/performing artsHumanitiesLaw/legal studiesCommunicationEducationSocial science/historyMAJOR FIELDBusinessPsychologyHealth sciencesComputer/information scienceBiology/life sciencesEngineering/math/physical sciencesNotes: Majors are ordered from left to right by ratings of science content. A score of 0 indicates no science-male implicit bias. Lower numbers on <strong>the</strong> y axis represent lowerscience-male implicit bias scores.Source: Smyth, Greenwald et al. (2015). Adapted with permission from Frederick L. Smyth.move into <strong>the</strong> science-is-female portion of<strong>the</strong> chart. They’re still solidly in <strong>the</strong> scienceis-maleportion.Yet even though women in engineering andcomputing associated science with male morereadily than science with female, <strong>the</strong>y had weakerimplicit science-male biases than women in nonscientificfields had. Supporting this finding, ano<strong>the</strong>rstudy showed that women with weaker math-maleimplicit biases <strong>report</strong>ed more participation in math,more self-ascribed ma<strong>the</strong>matical skill, and higherscores on <strong>the</strong> SAT and ACT math tests comparedwith women who had stronger math-male implicitbiases (Nosek & Smyth, 2011). Study after studyhas found that women in engineering and computingtend to have weak implicit associations betweenscience/math and male (Nosek & Smyth, 2011;Smyth, Greenwald et al., 2015; Smyth, Guilford etal., 2011).These findings cannot explain which camefirst—implicit bias or college major choice. Doweak implicit science-male biases lead womento major in engineering or computing, or whenwomen major in engineering or computing, do<strong>the</strong>ir science-male biases weaken? This researchdoesn’t answer <strong>the</strong>se questions, but researcherssuspect that both happen. Implicit biases probably
58SOLVING THE EQUATIONImplicit bias is a goodpredictor of college majorImplicit science-male bias has been found to bea stronger predictor of majoring in a STEM fieldthan has explicit bias (Nosek & Smyth, 2011; Nosek,Banaji et al., 2002a) or an individual’s score on <strong>the</strong>math SAT, an indicator often used to identify potentialfor achievement in <strong>the</strong>se areas. The correlationis in opposite directions for women and men.For women, weaker science-male bias is associatedwith majoring in STEM fields, whereas formen, stronger science-male bias is associated withmajoring in <strong>the</strong>se fields. Importantly, <strong>the</strong> correlationbetween level of implicit bias and majoring ina STEM field is as powerful for individuals who testhighest on standardized math tests as for thosewho test lowest.Smyth and his colleagues were not surprised tofind that implicit biases more accurately predictedmajoring in a STEM field than explicit biases didbecause <strong>the</strong> biases we <strong>report</strong> are subject to ourown limited abilities to see ourselves clearly andmotivations to present ourselves in a favorablelight. Implicit biases, in contrast, are more difficultto mask.influence <strong>the</strong> choices that women make, while at<strong>the</strong> same time, <strong>the</strong> environments in which womenare immersed shape <strong>the</strong>ir implicit biases (Nosek& Smyth, 2011). Regardless of <strong>the</strong> causal direction,<strong>the</strong>se studies illuminate a strong relationshipbetween implicit biases and <strong>the</strong> pursuit andattainment of science, engineering, and computingdegrees—positive for men and negative for women.Explicit science-male biasThis study also found that men who had majored inengineering, computing, math, or a physical science<strong>report</strong>ed <strong>the</strong> highest levels of explicit science-malebias as well. On a questionnaire, on average, menwho majored in scientific fields such as engineeringand computing <strong>report</strong>ed stronger consciousscience-male associations than o<strong>the</strong>r women andmen did, including women in engineering andcomputing (see figure 17). One possible explanationfor this is that some of <strong>the</strong> explicit associationspeople <strong>report</strong> are based on dominant patternsin <strong>the</strong>ir environment. For example, as shown infigure 17, women in health sciences and biologyhave lower explicit science-male biases than o<strong>the</strong>rwomen do, probably because male-to-female ratiosare much lower in health and biological sciencesthan in o<strong>the</strong>r scientific fields. In engineering andcomputing, on <strong>the</strong> o<strong>the</strong>r hand, male-to-femaleratios are much higher than in o<strong>the</strong>r fields, andmen in <strong>the</strong>se fields <strong>report</strong> higher science-malebiases than o<strong>the</strong>r men do.How gender biases affectengineering and computingenvironmentsIn a sense <strong>the</strong>se findings are not particularlysurprising. It is logical that men who major inengineering, computing, and science associate<strong>the</strong>mselves with science and so more readilyassociate science with men more generally. By <strong>the</strong>same token, it is logical that women who majorin engineering, computing, and science associate<strong>the</strong>mselves with <strong>the</strong>se fields and so are less likelyto readily associate science with men. Yet eventhough <strong>the</strong>se findings are not particularly surprising,<strong>the</strong>y beg an important question: What is <strong>the</strong>impact on women’s representation in engineeringand computing of classrooms and workplaces madeup primarily of men with strong math-male andscience-male implicit biases? According to Smyth,we still have a lot to learn about that.While ample evidence shows that implicit biasinfluences perceptions of women in science andmath, little research specifically links IAT scoreswith discriminatory behavior in <strong>the</strong> area of womenin engineering and computing. Still, researchdescribed in chapter 3 provides some evidence thatimplicit math/science-male biases as measured by<strong>the</strong> IAT are associated with discriminatory behaviortoward women (Reuben et al., 2014a).The research of Smyth and his colleaguesfound that men who majored in engineering
AAUW 59FIGURE 17. AVERAGE SCIENCE-MALE EXPLICIT ASSOCIATION SCORE, BY GENDER AND COLLEGE MAJOR1.6WomenMen1.41.2Explicit science-male score(standard deviations from zero)1.00.80.60.40.20.0Visual/performing artsHumanitiesLaw/legal studiesCommunicationEducationSocial science/historyMAJOR FIELDBusinessPsychologyHealth sciencesComputer/information scienceBiology/life sciencesEngineering/math/physical sciencesNotes: Majors are ordered from left to right by ratings of science content. A score of 0 indicates no science-male explicit bias. Lower numbers on <strong>the</strong> y axis represent lowerscience-male explicit bias scores.Source: Smyth, Greenwald et al. (2015). Adapted with permission from Frederick L. Smyth.and computing had some of <strong>the</strong> highest levels ofscience-male implicit biases of anyone majoring inany field and <strong>the</strong> highest levels of explicit sciencemalebiases of all (Smyth, Greenwald et al., 2015).Smyth told AAUW, “Should we be very afraid? Idon’t think so, any more than we should alreadybe working hard to create inviting, welcomingenvironments and continuing to learn as much aswe can about what facilitates a sense of belongingamong people who don’t feel as well represented ina particular domain.”Reducing discriminatory effectsof implicit biasesReducing possible discriminatory effects of implicitbiases requires organizations to institute policiesand practices that make hiring and promotioncriteria explicit, because without clearly definedcriteria, implicit bias can easily creep into decisionmaking. According to Smyth:Our best advice for counteracting <strong>the</strong>effects of implicit bias is for organizationsto do sustained work to create welcoming,
60SOLVING THE EQUATIONequality-focused environments. Educatingpeople about implicit bias, giving <strong>the</strong>m asense of <strong>the</strong> literature and some of <strong>the</strong>se fascinatingstudies in itself accomplishes little.Following <strong>the</strong> advice of Tony Greenwaldand Mahzarin Banaji in <strong>the</strong>ir book Blindspot[Banaji & Greenwald, 2013], we recommendthat organizations put into place “nobrainers”that minimize subjective aspectsof decision making about people, becausewe are always at risk of being influenced bybiases that we don’t know we have.Putting in place “no-brainers” means removingopportunities for bias to influence our decisions.For example, as much as possible, organizationsshould remove information about an individual’sage, race, or gender from decision-making contexts.Smyth notes that it takes concerted effortto create and maintain environments that arewelcoming:If you look around and see portraits on <strong>the</strong>wall in <strong>the</strong> engineering conference roomthat are 19 men and one woman, thatsends a message that this is a male bastion.Organizations can think creatively abouthonoring <strong>the</strong>ir legacy of male leaders with acontemporary reality that includes women.It’s tricky, and we need to be always thinkingcreatively because <strong>the</strong>re isn’t any clear, slamdunkmethod that’s yet been developed forchanging implicit biases.Changing implicit biasesThe idea of changing implicit biases indeed seemsto be a formidable task. How can you change somethingyou’re not even aware that you have? Yetwomen in engineering and computing have weakerscience-male implicit biases than women in nonscientificfields do. Therefore, reducing math-male andscience-male implicit biases, especially among girls,seems like an important avenue to explore to makeengineering and computing careers true optionsfor girls and women. Fortunately, because implicitattitudes are linked to <strong>the</strong> environment, researchers<strong>the</strong>orize that “such biases should shift when peopleare immersed in different types of situations where<strong>the</strong>y encounter admired and counter stereotypicalindividuals who do not fit <strong>the</strong>ir prescribed role insociety” (Dasgupta, 2013, p. 241).One recent experiment provides support for<strong>the</strong> notion that implicit biases are indeed malleable.Nilanjana Dasgupta, a psychology professorat <strong>the</strong> University of Massachusetts, Amherst,and her colleagues found that college women whohad a female as opposed to a male instructor fora calculus course had significantly more positiveimplicit attitudes toward math and strongerimplicit self-identification with math after <strong>the</strong>course than before <strong>the</strong> course (Stout, Dasgupta etal., 2011). 4 Women’s explicit beliefs about math didnot change even though <strong>the</strong>ir implicit associationsdid. Dasgupta (2013, p. 264) explained:In talking to participants at <strong>the</strong> end of <strong>the</strong>study, it was eminently clear that <strong>the</strong>sewomen were unaware that <strong>the</strong> people <strong>the</strong>ycame in contact with in class … had anyeffect on <strong>the</strong>ir own academic self-conceptand career goals. Like most people, participantsdescribed <strong>the</strong>ir academic interests asdriven mostly by <strong>the</strong>ir intrinsic interest andmotivation. They were unaware of <strong>the</strong> profoundeffects <strong>the</strong>ir local environments werehaving on <strong>the</strong>ir intellectual self-concept andcareer trajectories.Importantly, while women had more positiveimplicit attitudes toward math and strongerimplicit self-identification with math after taking acalculus course taught by a woman, women’s mathmaleimplicit biases did not change. Never<strong>the</strong>less,according to Smyth, <strong>the</strong> changes in attitudes andself-identification are <strong>the</strong>oretically important precursorsto changes in math-male implicit biases:Changes in implicit attitudes toward mathand self-identification with math are relatedto changes in implicit gender biases towardmath. If we make substantial changes inimplicit self-concept about a domain [in thiscase, ma<strong>the</strong>matics], <strong>the</strong>n we ought to see acorresponding change in <strong>the</strong> stereotype.The message from Dasgupta’s study is thatfemale role models, specifically teachers, can changegirls’ and women’s attitudes and identification
AAUW 61with math and science. Researchers suggest that<strong>the</strong>se changes will ultimately lead to changes inimplicit biases. O<strong>the</strong>r research by Dasgupta andher colleagues clarifies that relatability is a necessaryelement for role models to be effective. Femaleexperts portrayed as “superstars” who are uniqueand exceptional have little impact—and sometimeshave a deflating effect—on young women’s viewsof <strong>the</strong>mselves. Likewise, role models with whomyoung women do not identify have little effect onwomen’s self-concepts even if <strong>the</strong>y are in frequentcontact. Dasgupta’s research suggests that changingimplicit self-concepts (and eventually implicitbiases) requires both frequent exposure to femalerole models as well as feeling a connection with<strong>the</strong>se role models (Asgari et al., 2010; Dasgupta,2013).What can we do?The effect on <strong>the</strong> engineering and computingworkplace and education environments of so manymen with strong science-male implicit biases isunknown and is an area ripe for future research.Most women in engineering and computing, incontrast to <strong>the</strong>ir male colleagues, tend to have relativelyweak science-male implicit biases. Becauseof this, it makes sense to consider ways to reducegirls’ and women’s science-male implicit biases as apotential way to increase <strong>the</strong> chances that girls andwomen will develop an interest in <strong>the</strong>se fields.While research has yet to identify a clear-cutmethod for reducing implicit biases, female rolemodels with whom women can identify may help.Female role models have been shown to streng<strong>the</strong>nyoung women’s math attitudes and self-concepts—precursors to reducing gender-math and genderscienceimplicit biases—and increase girls’ andwomen’s abilities to truly consider engineeringand computing fields as viable career options. Inaddition, organizations should create welcomingenvironments for women. This includes activelydeveloping female leaders and training managersto run productive, inclusive teams. Organizationsshould also make hiring and promotion criteriaexplicit and examine <strong>the</strong>se criteria for implicitbiases. Finally, organizations should make <strong>the</strong> firstround of hiring, awards, and promotion discussionsgender-, disability-, and race-blind when possible.Chapter 4 notes1. Project Implicit is a popular website at which visitors can participate in studies and learn about implicit social cognition relatedto a variety of topics. Thousands of individuals visit <strong>the</strong> website each week. While participants in this study were not randomlychosen, Smyth told AAUW that he’s conducted similar, smaller studies with representative samples of participants and hasconsistently found patterns identical to that shown here, streng<strong>the</strong>ning <strong>the</strong> credibility of this pattern.2. A science-male IAT score could just as accurately be called a liberal arts-female score since both stereotypes are combined in IATmethodology.3. Nosek & Smyth (2011) found similar results using <strong>the</strong> gender-math IAT.4. Participants’ implicit attitude toward math was assessed by measuring how quickly participants categorized words related tomath, such as “algebra” or “equation,” with positive versus negative words, such as “joy” or “filth.” Participants’ implicit self-identificationwith math was assessed by measuring how quickly participants paired <strong>the</strong> same math words with first-person pronouns,such as “me” or “myself,” compared with third-person pronouns, such as “<strong>the</strong>y” or “<strong>the</strong>m” (Stout, Dasgupta et al., 2011).
Chapter 5.Stereotype Threat in<strong>the</strong> WorkplaceAfter women run <strong>the</strong> gauntlet of standardized testingand make it into graduate programs or even careers inSTEM, <strong>the</strong>y still find <strong>the</strong>mselves in contexts where <strong>the</strong>concern that <strong>the</strong>y might be judged through <strong>the</strong> lens of astereotype can rear its head.—Toni Schmader
64SOLVING THE EQUATIONWomen presented with <strong>the</strong> stereotype that menare better at math perform worse on difficult mathtests than women who are told that <strong>the</strong>re are nogender differences in math performance (Spenceret al., 1999). This lowered performance is a resultof a phenomenon called “stereotype threat,” ora fear of confirming a negative stereotype aboutyour group (Steele, 1997). Hundreds of studieshave verified <strong>the</strong> influence of stereotype threat inmany domains, including academic performanceamong black students (Steele & Aronson, 1995;Taylor & Walton, 2011), memory in older adults(Hess, T. M., et al., 2003), girls’ chess performance(Rothgerber & Wolsiefer, 2014), and women’sathletic performance (Hively & El-Alayli, 2014). Inevery case even subtle reminders of negative stereotypescan have an impact on performance, sometimesin dramatic ways. For example, accordingto a meta-analysis by Walton and Spencer (2009),stereotype threat results in an underestimation of<strong>the</strong> intellectual ability of black and Latino studentsby approximately 40 points on <strong>the</strong> SAT math andreading tests.Stereotype threat is thought to undermineperformance in part by reducing working memorycapacity as individuals use some of <strong>the</strong>ir finitecognitive resources to suppress negative emotionsor knowledge of a stereotype to focus on a task(Croizet et al., 2004; Schmader & Johns, 2003).Stereotype threat has been shown to increase stressand anxiety (Blascovich et al., 2001; Bosson et al.,2004; Inzlicht & Ben-Zeev, 2003) and is <strong>the</strong>orizedto ultimately lead to disidentification and disengagementfrom domains in which a person feelsstereotyped (Steele, 1997).A study by University of Waterloo psychologyprofessor Christine Logel and her colleagues(2009) explored whe<strong>the</strong>r interpersonal interactionsoutside <strong>the</strong> classroom can create stereotype threatconditions for women in a male-stereotyped field.The researchers found that interacting with subtlysexist male peers caused women who were majoringin math, science, or engineering to experiencestereotype threat. Men holding sexist attitudesrevealed <strong>the</strong>ir sexism through subtle but consistentbehaviors, including behaving in more dominantand flirtatious ways. After interactions with <strong>the</strong>semen, women subsequently performed worse on anengineering or math test (but not an English test)than did women who interacted with nonsexistmen. 1How stereotype threataffects women in <strong>the</strong>workplaceResearchers have only recently begun to examinehow stereotype threat operates in <strong>the</strong> workplace(Kalokerinos et al., 2014). Most research to dateon stereotype threat has focused on academicperformance (Walton & Spencer, 2009). By virtueof its relationship with stress, anxiety, and disengagement,however, stereotype threat could alsolead to a wide variety of negative experiences forstereotyped individuals at work. Engineering andcomputing workplaces, in which women potentiallyface an almost constant threat of confirming <strong>the</strong>stereotype that men are better suited for scienceand math, are prime work environments in whichto explore stereotype threat.Toni Schmader, a psychologist at <strong>the</strong> Universityof British Columbia, and her colleagues ShannonHolleran, Jessica Whitehead, and Matthias Mehlare among <strong>the</strong> first researchers to study stereotypethreat in <strong>the</strong> workplace. Moving beyond strictissues of performance, <strong>the</strong>se researchers conductedan experiment that explored whe<strong>the</strong>r experiencesof stereotype threat might relate to disidentificationor disengagement in <strong>the</strong> workplace. In aninterview with AAUW, Schmader explained how<strong>the</strong>y shifted <strong>the</strong>ir focus from <strong>the</strong> classroom to <strong>the</strong>professional field:As we tried to understand <strong>the</strong> lower representationof women in science and technologyfields more generally, it became apractical but also a <strong>the</strong>oretically interestingquestion as to why <strong>the</strong> effects of stereotypethreat would be limited to <strong>the</strong> earlier stagesof <strong>the</strong> pipeline, when people are getting <strong>the</strong>ireducation and <strong>the</strong>ir credentials. There arereasons why you might think that peoplewould have good coping strategies for dealingwith stereotype threat by <strong>the</strong> time <strong>the</strong>yget to a professional environment. But we
AAUW 65suspected that <strong>the</strong>re were still ways in whichstereotype threat would be experienced inworkplace environments, and so we set outto test that.Moving from <strong>the</strong> laboratory to <strong>the</strong> workplacemeant that Schmader and her colleagues needed anew way to identify when female scientists experiencedstereotype threat in <strong>the</strong> workplace and howto measure changes in performance. The researchershypo<strong>the</strong>sized that stereotype threat could comeinto play for female science and engineering facultywhen <strong>the</strong>y talked about <strong>the</strong>ir research with malecolleagues. Schmader told AAUW why <strong>the</strong>y testedstereotype threat within conversations:Science faculty members don’t take standardizedtests on a regular basis. So wheredo we feel like our ideas are tested? They’reobviously tested in reviews of articlessubmitted to journals, but <strong>the</strong>y can also betested in conversational contexts, and that’swhat led us to look at conversations <strong>the</strong>mselvesas potential triggers of stereotypethreat.To capture whe<strong>the</strong>r conversations could triggerstereotype threat, 37 faculty members fromSTEM departments at a large public universitycarried portable recording devices for four days.The devices recorded 50 seconds of conversationevery 9 minutes from 6 a.m. to 11 p.m. Theresearch team transcribed <strong>the</strong> resulting conversationsthat took place between colleagues during <strong>the</strong>work day, coding <strong>the</strong> gender of <strong>the</strong> speakers andwhe<strong>the</strong>r <strong>the</strong> conversation concerned research orsocial matters. Two researchers also independentlyrated each conversational snippet for <strong>the</strong> speaker’slevel of competence, likeability, and contribution to<strong>the</strong> conversation. Researchers combined conversationdata with a questionnaire on job engagement(Holleran et al., 2011).Research conversations andstereotype threatSchmader and her colleagues found that <strong>the</strong> morewomen discussed research with male colleagues, <strong>the</strong>less engaged <strong>the</strong>y <strong>report</strong>ed feeling with <strong>the</strong>ir work.Men, on <strong>the</strong> o<strong>the</strong>r hand, showed <strong>the</strong> expectedrelationship of being more engaged with <strong>the</strong>ir workwhen <strong>the</strong>y had more conversations about researchwith <strong>the</strong>ir male colleagues (see figure 18). 2 In addition,researchers coded <strong>the</strong> female scientists, onaverage, as sounding less competent than <strong>the</strong>ir malepeers during research conversations with o<strong>the</strong>rmale colleagues. The researchers found no genderdifferences in perceived competence during anyo<strong>the</strong>r situations.The relationship between social conversatio<strong>nsa</strong>nd engagement also differed by gender. In contrastto research conversations, social conversations withmale colleagues were related to more engagementfor female scientists (see figure 19). Social conversationsbetween two male colleagues or two femalecolleagues, on <strong>the</strong> o<strong>the</strong>r hand, were related to lessengagement, which is what one might expect sincemore time socializing at work means less timeworking.While researchers cannot draw definitiveconclusions from <strong>the</strong>ir correlational data, Schmaderand her colleagues hypo<strong>the</strong>size that research conversationswith men may cue stereotype threat forfemale scientists. Social conversations with malecolleagues, on <strong>the</strong> o<strong>the</strong>r hand, do not appear toactivate a negative stereotype of women in STEMand may actually lessen <strong>the</strong> threat women feel inmale-dominated environments by increasing a feelingof belonging (Holleran et al., 2011). Far from<strong>the</strong> last word on <strong>the</strong> subject, <strong>the</strong>se findings point toadditional questions about how stereotype threatoperates and affects women in typically male worksettings.O<strong>the</strong>r research supports <strong>the</strong> potential forstereotype threat and negative work outcomeswhen women compare <strong>the</strong>mselves to men in <strong>the</strong>workplace. Courtney von Hippel, a senior lecturerin psychology at <strong>the</strong> University of Queensland inAustralia, and her colleagues conducted studiesof stereotype threat in <strong>the</strong> workplace that foundthat women who compared <strong>the</strong>mselves to menin <strong>the</strong> same organization experienced stereotypethreat (von Hippel, Issa et al., 2011). Experiencesof stereotype threat, in turn, were associated witha separation of a “female” identity and a “work”identity (von Hippel, Walsh et al., 2011), decreased
66SOLVING THE EQUATIONFIGURE 18. RESEARCH CONVERSATIONS WITHMALE COLLEAGUES AND LEVEL OF JOBDISENGAGEMENT, BY GENDERFIGURE 19. SOCIAL CONVERSATIONS WITHMALE COLLEAGUES AND LEVEL OF JOBDISENGAGEMENT, BY GENDER2.502.25WomenMen2.502.25WomenMenDisengagement from job(scale from 1 to 4)2.001.751.501.25Disengagement from job(scale from 1 to 4)2.001.751.501.251.00Fewer conversationsMore conversations1.00Fewer conversationsMore conversationsRESEARCH CONVERSATIONS WITH MALE COLLEAGUESSOCIAL CONVERSATIONS WITH MALE COLLEAGUESNotes: On average, study participants’ conversations with <strong>the</strong>ir male colleagues wereabout research 47 percent of <strong>the</strong> time. The “fewer” value represents 19 percent, in o<strong>the</strong>rwords, study participants who talked about research 19 percent of <strong>the</strong> time when <strong>the</strong>yspoke with <strong>the</strong>ir male colleagues. The “more” value represents 77 percent, in o<strong>the</strong>rwords, women and men who talked about research 77 percent of <strong>the</strong> time <strong>the</strong>yconversed with <strong>the</strong>ir male colleagues. Nineteen percent is one standard deviation lowerthan <strong>the</strong> average value, and 77 percent is one standard deviation higher than <strong>the</strong>average value (AAUW communication with Toni Schmader).Source: Holleran et al. (2011).Notes: On average, study participants’ conversations with <strong>the</strong>ir male colleagues wereabout social topics 23 percent of <strong>the</strong> time. The “fewer” value represents 5.5 percent, ino<strong>the</strong>r words, study participants who talked about social topics 5.5 percent of <strong>the</strong> timewhen <strong>the</strong>y spoke with <strong>the</strong>ir male colleagues. The “more” value of social conversationswith male colleagues represents 40.5 percent, in o<strong>the</strong>r words, study participants whotalked about social topics 40.5 percent of <strong>the</strong> time when <strong>the</strong>y conversed with <strong>the</strong>irmale colleagues. Five and one-half percent is one standard deviation lower than <strong>the</strong>average value, and 40.5 percent is one standard deviation higher than <strong>the</strong> average value(AAUW communication with Toni Schmader).Source: Holleran et al. (2011).perceived likelihood of achieving career goals,reduced job satisfaction, and greater intentions toquit (von Hippel, Issa et al., 2011).Limitations and upcomingresearchUnderstanding <strong>the</strong> implications of stereotypethreat in <strong>the</strong> workplace is important. Stereotypethreat may be a key to why many women leaveengineering and computing jobs, since disengagementis <strong>the</strong>orized to be a hallmark response tostereotype threat over time (Aronson et al., 2002;Steele, 1997). To gain a better understanding ofhow stereotype threat affects women in typicallymale work environments, Schmader and her colleaguesare now exploring stereotype threat amongprofessional engineers. This research will add to<strong>the</strong>ir earlier findings by specifically measuring professionalengineers’ concern about being evaluatedbased on <strong>the</strong>ir gender. Preliminary findings suggestthat conversations with male colleagues that cuefeelings of inau<strong>the</strong>nticity or a lack of acceptanceare associated with an increase in gender awareness,which in turn correlates with higher levelsof mental exhaustion and lower job commitment(Hall et al., in press). Understanding more abouthow stereotype threat operates in <strong>the</strong> workplaceis an important area for future investigation asresearchers continue to explore <strong>the</strong> factors behind<strong>the</strong> underrepresentation of women in engineeringand computing.What can we do?Schmader’s ongoing research is finding thatgender-inclusive policies are associated with fewerexperiences of stereotype threat as well as greaterorganizational commitment and life satisfactionamong female professional engineers (Hall et al.,in press). Examples of gender-inclusive policies
AAUW 67include efforts to reduce sexual harassment at work,policies that reduce work-family conflict, and useof gender-inclusive language. Employers shouldbe careful about <strong>the</strong> language used in job postingsbecause language can make a difference in whoapplies for positions. Job postings and descriptionsshould be gender neutral and use wordsthat include feminine strengths and skill sets. Jobadvertisements, mission statements, and internalcommunications should explicitly convey that anorganization values diversity and gender inclusiveness.Finally, increasing <strong>the</strong> number of womenin <strong>the</strong> workplace at all levels of management canreduce <strong>the</strong> prevalence of stereotype threat. Researchsuggests that stereotypes are activated for womenmore frequently when men significantly outnumberwomen (Inzlicht & Ben-Zeev, 2000; Murphy et al.,2007).Chapter 5 notes1. Logel and her colleagues inferred that women experienced stereotype threat in this experiment because women <strong>report</strong>ed suppressingconcerns about gender stereotypes. Suppressing <strong>the</strong>se concerns is an established mechanism of stereotype threat that canlead to lowered performance and o<strong>the</strong>r outcomes.2. Schmader and her colleagues found no significant relationship between engagement and research conversations with female colleagues,although conclusions were tempered by <strong>the</strong> low number of conversations between female colleagues in <strong>the</strong> study.
Chapter 6.Making <strong>the</strong> Worlda Better PlaceCommunal goals are highly valued generally by society.While we have good reason to suspect that bringing in <strong>the</strong>communal aspect of engineering and computing mightbe especially effective at attracting and retaining girlsand women, we have also never shown that it dissuadesmen or boys. To me that speaks to <strong>the</strong> potential powerof this approach as a lever to bring more people into<strong>the</strong>se fields.—Amanda Diekman
70SOLVING THE EQUATIONSome researchers argue that <strong>the</strong> underrepresentationof women in engineering and computing maybe explained in part by <strong>the</strong> perception that <strong>the</strong>sefields lack an emphasis on communal goals. Ino<strong>the</strong>r words, because engineering and computingoccupations seem less likely than o<strong>the</strong>r professionalfields to involve collaboration or a direct benefitto o<strong>the</strong>rs, <strong>the</strong>y may be less appealing to manywomen—and to many men as well.Two studies led by Miami University psychologyprofessor Amanda Diekman and her colleaguesElizabeth Brown, Amanda Johnston, EmilyClark, and Mia Steinberg addressed <strong>the</strong> potentialimpact of gender differences in “communal” and“agentic” values on <strong>the</strong> representation of womenin STEM fields such as engineering and computing.Psychologists use <strong>the</strong>se two terms to describecontrasting fundamental orientations of humanexperience: Communal motivations describe anorientation to o<strong>the</strong>rs and are associated with maintenanceof positive relationships; agentic motivatio<strong>nsa</strong>re associated with self-advancement in socialhierarchies, making one’s own free choices, andacting independently (Trapnell & Paulhus, 2012).This chapter considers <strong>the</strong> degree to which <strong>the</strong>seconcepts help us understand women’s underrepresentationin engineering and computing.The motivation to help o<strong>the</strong>rs is widely valued.In much of <strong>the</strong> world, including <strong>the</strong> United States,<strong>the</strong> majority of people—both women and men—endorse communal values, such as working for <strong>the</strong>well-being of o<strong>the</strong>rs, caring for <strong>the</strong> disadvantaged,and responding to <strong>the</strong> needs of o<strong>the</strong>rs as <strong>the</strong>ir mostimportant guiding principles (Schwartz & Bardi,2001, as summarized in Grant, 2013). Individualswhose jobs allow <strong>the</strong>m closer contact with <strong>the</strong> beneficiariesof <strong>the</strong>ir work <strong>report</strong> greater motivationand exhibit greater persistence in <strong>the</strong>ir jobs (Grant,2007; Grant et al., 2007), and people generally tendto positively evaluate those who are described ascommunally oriented (Diekman, 2007; Prentice &Carranza, 2002).Women and communalvaluesThe idea that women are more motivated than mento pursue work that helps people is a stereotype.The purpose of highlighting this research is notto perpetuate <strong>the</strong> stereotype but to explore <strong>the</strong>possible ramifications of this small gender differencefor women’s representation in engineering andcomputing. Of course, not all women are motivatedto <strong>the</strong> same extent by <strong>the</strong> same goals (Quesenberry& Trauth, 2012), and certainly many men prioritizecommunal values more highly than many womendo. None<strong>the</strong>less, on average, women are more likelythan men to say that <strong>the</strong>y prefer work with a clearsocial purpose (Eccles, 2007; Lubinski & Benbow,2006; Pöhlmann, 2001; Konrad et al., 2000;Margolis, Fisher, & Miller, 2002). Even girls andyoung women are more likely than boys and youngmen to say that <strong>the</strong>y want “helping o<strong>the</strong>rs” to be animportant aspect of <strong>the</strong>ir future jobs ( Jones et al.,2000; Weisgram, Bigler et al., 2010).Working with andhelping peopleDiekman and her colleagues included two motivationswithin <strong>the</strong>ir definition of communal goalorientation: collaboration and helping (Diekman,Weisgram et al., 2015; Diekman & Steinberg, 2013).Although <strong>the</strong>se concepts are separate and distinct,much of <strong>the</strong> evidence so far suggests that individuals’endorsements of <strong>the</strong>se components of communalgoals tend to align with each o<strong>the</strong>r (Diekman,Weisgram et al., 2015). Diekman and her colleaguesare beginning to conduct research that teases apart<strong>the</strong>se two motivations to gain a more fine-grainedunderstanding of <strong>the</strong> influence of each element onvarious outcomes, but <strong>the</strong> latest research on thistopic combines <strong>the</strong> two motivations (Diekman &Steinberg, 2013).
AAUW 71How traditional gender rolesaffect valuesValues are not altoge<strong>the</strong>r freely chosen. Socialforces may teach girls and women to value helpingo<strong>the</strong>rs. Research shows that women are expectedto be nurturing and o<strong>the</strong>r-oriented and, unlikemen, are penalized when <strong>the</strong>y don’t behave inaltruistic ways (Chen, J. J., 2008; Heilman & Chen,2005; Heilman, 2001; Heilman & Okimoto, 2007;Rudman & Glick, 2001; Rudman, Moss-Racusinet al., 2012). For men, who have traditionally occupiedprovider and leadership roles, self-orientedbehavior, such as earning money and gainingpower, are especially valued (Eagly & Wood, 2012;Eagly, 1987).Women’s and men’s prioritiesAs women have advanced in educational achievementsand labor force participation, <strong>the</strong>y havebecome more similar to men in <strong>the</strong>ir motivationfor self-advancement. By some measures, womenare even more achievement-oriented than menare today. For example, in a recent survey of youngadults ages 18 to 34, women were more likely thanmen to say that being successful in a high-payingcareer or profession is very important or one of <strong>the</strong>most important things in <strong>the</strong>ir lives, representing agender reversal compared with responses to a similarsurvey conducted in <strong>the</strong> late 1990s (Patten &Parker, 2012). This example demonstrates <strong>the</strong> effectthat cultural factors have on individuals’ goals, sinceany biological factors related to motivation for selfadvancementcould not have changed much in <strong>the</strong>overall population in <strong>the</strong> past two decades. Whilewomen and men have converged in how much <strong>the</strong>yvalue self-advancement, during <strong>the</strong> same period,gender differences in <strong>the</strong> value placed on workingwith and helping o<strong>the</strong>rs have remained relativelystable and moderate in size (Eagly & Diekman,2003; Twenge, 1997).Perceptions of engineeringand computingEngineering and computing occupations are generallyperceived as unengaged with societal and communityconcerns and involving little contact withPerceived likelihood of goal fulfillment(scale from 1 to 7)FIGURE 20. PERCEIVED COMMUNAL ANDAGENTIC GOAL FULFILLMENT,BY TYPE OF CAREER76543210Engineering/computing/scienceSource: Diekman, Brown et al. (2010).Traditionallymale non-STEMCAREER TYPEAgentic goalsCommunal goalsTraditionallyfemaleo<strong>the</strong>r people (National Academy of Engineering,2008). In two studies Diekman and her colleagueslooked at how perceptions of <strong>the</strong>se fields mightaffect women and men differently. In <strong>the</strong> first study<strong>the</strong> researchers asked 360 undergraduate studentsto rate a combination of STEM, non-STEMtraditionally male careers, and traditionally femalecareers 1 by how much <strong>the</strong>y would allow individualsto fulfill communal and agentic goals. Studentswere asked to estimate how well a career fulfilledagentic goals as well as how well it fulfilled communalgoals. 2 The findings were clear: Participantsrated engineering, computing, and science careersas less likely to fulfill communal goals than traditionallyfemale careers or traditionally male non-STEM careers (see figure 20).Career interestsParticipants <strong>the</strong>n rated <strong>the</strong> goals according tohow important each goal was to <strong>the</strong>m personallyon a scale ranging from 1 (not at all important)to 7 (extremely important) and rated <strong>the</strong>ir career
72SOLVING THE EQUATIONFIGURE 21. CAREER INTEREST AS A FUNCTION OFENDORSEMENT OF COMMUNAL GOALSCareer interest76543210Traditionallyfemale careersLowerTraditionally malenon-STEM careersHigherCOMMUNAL GOAL ENDORSEMENTNote: Scale from 1 (not at all) to 7 (extremely interested).Source: Diekman, Brown et al. (2010).Engineering/computing/scienceinterest in each of a number of STEM, traditionallymale non-STEM, and traditionally femalecareers. Diekman and her colleagues found that <strong>the</strong>more individuals endorsed communal goals, <strong>the</strong>more interest <strong>the</strong>y expressed in traditionally femalecareers and <strong>the</strong> less interest <strong>the</strong>y expressed in engineeringand computing careers. The trend shownin figure 21 is consistent with previous researchindicating that individuals who pursue engineeringand science careers on average place an unusuallylow value on having a job that directly benefitso<strong>the</strong>r people or society (Eccles, 2007).Past achievement and confidenceIn <strong>the</strong> next part of <strong>the</strong> study, Diekman and hercolleagues assessed <strong>the</strong> participants’ belief in <strong>the</strong>irability to succeed (self-efficacy) in engineeringand computing careers and estimated participants’experience in STEM subjects, using enrollmentin STEM classes. The researchers found that evenamong students with strong past STEM achievementand belief in <strong>the</strong>ir mechanical, computational,and scientific abilities, those who were more motivatedto work with and help people were less likelyto express interest in engineering and computingfields. Communal goal endorsement predicteda lack of interest in engineering and computing.Finally, like o<strong>the</strong>r studies, this study (Diekman,Brown et al., 2010) found that women, on average,placed greater importance than men did onworking with and helping people, and a follow-upstudy (Diekman, Clark et al., 2011) confirmed thisfinding (see figure 22). 3Why relative assessment mattersResearchers <strong>the</strong>orize that an individual’s relative,ra<strong>the</strong>r than absolute, assessment of her or hisabilities and values is <strong>the</strong> critical factor in selectinga career path (Eccles, 2011b; Evans & Diekman,2009). Both women and men value communalgoals, but unlike men, women, on average, rank<strong>the</strong>se goals higher than self-advancement goals.Even though <strong>the</strong> gender differences in both <strong>the</strong>desire for self-advancement and <strong>the</strong> desire to helpo<strong>the</strong>rs are small to moderate, <strong>the</strong>y matter whenit comes to career choices. If individuals tend tochoose <strong>the</strong> career that best suits <strong>the</strong>ir abilities andbest matches <strong>the</strong>ir values, it follows that women, onaverage, would be more likely to enter into what areperceived as helping professions and less likely topursue engineering and computing careers.Diekman is quick to point out that genderdifferences in motivation to work with peopleand to help people do not fully explain women’sunderrepresentation in engineering and computingfields. When she and her colleagues controlledFigure 22. Average Goal Endorsement,by GenderWomenMenCommunal goals 5.6 5.0Agentic goals 5.1 5.3Note: Scale from 1 (not at all important) to 7 (very important).Source: Diekman, Clark et al. (2011).
AAUW 73for communal goal orientation, <strong>the</strong> gender differencein interest in a STEM career narrowed,but it did not disappear. In an interview withAAUW, Diekman said, “One of my fears is thatthis research will be interpreted as suggestingthat gender differences in communal goals are <strong>the</strong>only thing that matters.” Without diminishing<strong>the</strong> importance of o<strong>the</strong>r factors such as workplaceculture, gender bias, and stereotypes, Diekman’sresearch suggests that understanding individuals’motivation to work with and help people is animportant part of <strong>the</strong> puzzle of why women are lesslikely than men to pursue careers in engineeringand computing.The image of engineeringand computingIf <strong>the</strong> opportunities for achieving communal goalsin engineering and computing careers were bettercommunicated to <strong>the</strong> public, might more girlsand women aspire to <strong>the</strong>se careers? Some indicatorssuggest that this might be true. For example,certain engineering disciplines with a clearer socialpurpose, such as biomedical engineering andenvironmental engineering, have had more successattracting a higher percentage of women than haveo<strong>the</strong>r disciplines, such as mechanical or electricalengineering (Yoder, 2013).Diekman, Clark, and <strong>the</strong>ir colleagues (2011)conducted a second study to test this hypo<strong>the</strong>sismore broadly. In this study 241 students from anintroductory psychology course were asked to readabout a day in <strong>the</strong> life of an entry-level scientist.Participants were randomly assigned to read aboutei<strong>the</strong>r a scientist whose tasks involved working withand helping o<strong>the</strong>rs or a scientist who completed<strong>the</strong> same tasks without working with and helpingo<strong>the</strong>rs. Some gender differences emerged inparticipants’ responses. Women who read about <strong>the</strong>scientist who worked with and helped o<strong>the</strong>rs weremore likely than o<strong>the</strong>r women to express a positiveattitude toward a science career. In contrast, amongmen no significant difference in positivity toward ascience career was found whe<strong>the</strong>r it was framed as acollaborative or an independent career.Thus, framing a science career as involvingmore collaboration and helping o<strong>the</strong>rs resulted inwomen being more positive about that career withoutturning men away. O<strong>the</strong>r research has foundthat, overall, students are more positive toward ascience career when <strong>the</strong>y perceive it to be a careerthrough which <strong>the</strong>y can help people (Weisgram &Diekman, 2014; Weisgram & Bigler, 2006).The reality of engineeringand computing careersDiekman’s second study suggests that showcasingcommunal aspects of engineering and computingcan change public perceptions. Engineeringand computing professionals have helped peopleand society in myriad ways, including improvingsanitation, fighting diseases, helping people withdisabilities, developing modes of transportationand infrastructure, connecting people around <strong>the</strong>world, tracking <strong>the</strong> spread of diseases, and developingsustainable forms of energy. In addition, manyengineers and computing professionals work inteams.Yet, it is still important to consider whe<strong>the</strong>rmost engineering and computing jobs involveworking with and helping people. What if <strong>the</strong>stereotype that <strong>the</strong>se are solitary jobs that don’tprovide opportunities for making a social contributionis mostly accurate? If that’s true, <strong>the</strong>n recruitingcommunally oriented women and men intoengineering and computing through marketingcampaigns touting <strong>the</strong> collaborative, helpful aspectsof <strong>the</strong>se fields will result in an exodus from <strong>the</strong> fieldlater. The reality, of course, is more important thanpublic perception.Some evidence suggests that engineering careers,in particular, fall short in providing opportunitiesfor fulfilling communal goals. One study of workingengineers found that women who expressed aninterest in social dimensions of work were morelikely than o<strong>the</strong>rs to want to leave <strong>the</strong>ir engineeringjobs (Fouad et al., 2012). Fletcher (1999) foundthat female engineers frequently helped o<strong>the</strong>rs,mentored o<strong>the</strong>rs, and went <strong>the</strong> extra mile in <strong>the</strong>interest of getting <strong>the</strong> job done, but this workwas frequently not recognized by o<strong>the</strong>rs in <strong>the</strong>
74SOLVING THE EQUATIONengineering firm as “real” work. Ra<strong>the</strong>r, o<strong>the</strong>rorientedbehaviors were viewed as outside <strong>the</strong> scopeof engineering work and misinterpreted as naturalexpressions of femininity. Most difficult for <strong>the</strong>female engineers, communal behaviors were bo<strong>the</strong>xpected of female engineers and devalued by <strong>the</strong>engineering culture.Ironically, Diekman’s research suggests thato<strong>the</strong>r-oriented activities (which are often viewed asseparate from and less valuable than <strong>the</strong> real workof engineering or computing) may be crucial forcreating and maintaining long-term engagementin engineering or computing work, especially forindividuals who are strongly motivated by communalgoals (Diekman, Weisgram et al., 2015).What can we do?Working with people and helping o<strong>the</strong>rs are valuedgoals for most people (Schwartz & Bardi, 2001),especially women (Diekman, Clark et al., 2011).The more that engineering and computing educatorsand employers can incorporate communalgoals into <strong>the</strong>ir environment, <strong>the</strong> more open <strong>the</strong>doors of <strong>the</strong>se fields will be to people, many ofwhom are women, who strongly value working withand helping o<strong>the</strong>rs.Employers, engineering and computing professionals,STEM teachers, parents, and anyoneseeking to motivate young people to considerengineering and computing careers can take somerelatively simple steps to put this research intopractice. Talk about <strong>the</strong> ways that engineers andcomputing professionals solve problems for society.Showcase how professionals’ everyday work alignswith <strong>the</strong> societally beneficial outcomes that are<strong>the</strong> ultimate goals of engineering and technology.Individual engineers and computing professionalscan work through professional associations to mentorstudents and new co-workers. Employers canencourage and value efforts and activities that fulfillcommunal goals while at <strong>the</strong> same time providingvalue for <strong>the</strong>ir organization. One possibility is toformally recognize <strong>the</strong> necessary nontechnical work(such as working well with o<strong>the</strong>rs and mentoring)as much as <strong>the</strong> necessary technical work in producinga product. Ano<strong>the</strong>r possibility is to put in placepaid social service days where employees volunteerin <strong>the</strong>ir community.Chapter 6 notes1. STEM careers rated by participants included mechanical engineer, aerospace engineer, computer scientist, and environmentalscientist. Traditionally male non-STEM careers included lawyer, physician, architect, and dentist. Traditionally femalecareers included preschool or kindergarten teacher, registered nurse, social worker, human resources manager, and educationadministrator.2. Some of <strong>the</strong> agentic goals that Diekman and her colleagues included were power, recognition, achievement, individualism, success,demonstrating skill or competence, and competition with o<strong>the</strong>rs. Some of <strong>the</strong> communal goals <strong>the</strong>y included were helpingo<strong>the</strong>rs, serving humanitarian needs, serving <strong>the</strong> community, working with people, and connecting with o<strong>the</strong>rs.3. In Diekman’s 2010 and 2011 studies, women endorsed communal goals more strongly than agentic goals and more strongly thanmen did. Unlike <strong>the</strong> 2011 study, however, <strong>the</strong> 2010 study found no statistically significant difference between men’s endorsementof communal goals and men’s endorsement of agentic goals: Men rated <strong>the</strong>m equally important.
Chapter 7.College Environmentand CurriculumPeople often say it’s about <strong>the</strong> students at HarveyMudd. I don’t think it’s just about <strong>the</strong> students. I thinkit’s about <strong>the</strong> unified efforts of <strong>the</strong> faculty and <strong>the</strong>president. If a school wants to graduate more womenwith computer science degrees, it can’t necessarily makeits whole student body look like Harvey Mudd students,but it certainly can make its faculty work toge<strong>the</strong>r.—Christine Alvarado
76SOLVING THE EQUATIONBecause many technical jobs require an engineeringor computer science degree, it is important tounderstand <strong>the</strong> effect of <strong>the</strong> engineering and computingcollege environment on women’s underrepresentationin those jobs. Many beginning collegestudents have little or no engineering or computingexperience, and <strong>the</strong> students who do have experienceoverwhelmingly tend to be men. Yet a numberof colleges and universities have succeeded in graduatinghigher than typical percentages of womenfrom <strong>the</strong>ir engineering and computing programs.Harvey Mudd College (HMC), a science- andengineering-focused liberal arts college with justunder 800 students in Claremont, California, iswidely recognized for dramatically increasing <strong>the</strong>percentage (and number) of women majoring in itscomputer science program from a historical averageof 12 percent to approximately 40 percent over <strong>the</strong>past five years (Alvarado & Judson, 2014) (see figure23). This percentage is well above <strong>the</strong> nationalaverage of 18 percent.Historically HMC, like almost all colleges anduniversities, had little success attracting women tocomputer science. In fact, until recently women atHMC were less well represented in computer sciencethan in any o<strong>the</strong>r field of study (Alvarado &Dodds, 2010); administrators and HMC professorsdecided to focus on increasing <strong>the</strong> representation ofwomen in <strong>the</strong> computer science program in 2006.Christine Alvarado, <strong>the</strong>n a computer science professorat HMC (now a faculty member in computerscience at <strong>the</strong> University of California, San Diego),and her colleagues Ran Libeskind-Hadas, ZachDodds, and Geoff Kuenning based <strong>the</strong>ir efforts ona perception that, for <strong>the</strong> most part, students withoutprior computer science experience do not reallyunderstand what computer science is.Alvarado and her colleagues believed that thislack of understanding was an important reason fewwomen expressed interest in computer science. Theprofessors hypo<strong>the</strong>sized that just by making clearto students what computer science is, <strong>the</strong>y couldincrease <strong>the</strong> number of female computer sciencemajors and dramatically improve <strong>the</strong> computerscience experience for all <strong>the</strong>ir students. To achievethis goal, HMC implemented three major changesfocused on first-year students, well in advance ofwhen most students declare a major at <strong>the</strong> school(Alvarado, Dodds et al., 2012):1. HMC revised <strong>the</strong> introductory computer sciencecourse that presents <strong>the</strong> breadth of <strong>the</strong>computer science field in addition to <strong>the</strong> basicsof programming.2. HMC provided research opportunities forwomen immediately after <strong>the</strong>ir first year ofcollege to expose <strong>the</strong>m to real computer scienceproblems as early as possible.3. HMC gave first-year students opportunities toattend <strong>the</strong> annual Grace Hopper Celebrationof Women in Computing conference hostedby <strong>the</strong> Anita Borg Institute for Women andTechnology.Computer sciencefor everyoneAll first-year students at HMC are required to takeand pass an introductory computer science class.Until 2005, everyone took <strong>the</strong> same Java-basedcourse, in which students were allowed to workahead to accommodate different levels of experience.Professors identified several problems during<strong>the</strong> course’s 10-year run. The class was too easy forsome and too difficult for o<strong>the</strong>rs (Alvarado, Doddset al., 2012). Additionally, students “were comingout kind of feeling <strong>the</strong> same way <strong>the</strong>y did when<strong>the</strong>y came in,” said Alvarado in an interview withAAUW. She and her colleagues worried that <strong>the</strong>students weren’t developing an understanding ofwhat computer science was—or how “cool” it was.The students “weren’t seeing what we’re seeingabout this field,” she said. The professors determinedthat students were missing <strong>the</strong> true natureof computer science and its major contributions too<strong>the</strong>r fields and society.These problems disproportionately affectedwomen because women, on average, come to HMCwith less computer science experience than men do(Alvarado & Judson, 2014). For this reason, womenwere apt to find <strong>the</strong> introductory computer scienceclass more difficult than <strong>the</strong>ir male colleaguesdid and were less likely to have a positive view ofcomputer science heading into <strong>the</strong> course. In addition,as described in chapter 6, women generally are
AAUW 77FIGURE 23. FEMALE COMPUTER SCIENCE GRADUATES NATIONALLY AND AT HARVEY MUDD COLLEGE,BY GRADUATION YEAR, 2000–2014605050%40Percentage3028% 28% 28%26%35% 35%38%2027% 25%22% 21%19% 18% 18% 18% 18% 18% 18%23%12%12%20%1010% 12% 6%13%14%3%02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013YEAR OF GRADUATION2014Female computer science graduates nationallyFemale computer science graduates at Harvey Mudd CollegeSources: Harvey Mudd College Office of Institutional Research; AAUW analysis of data from National Science Foundation, Division of Science Resources Statistics (2013); L. M.Frehill analysis of data from National Science Foundation, National Center for Science and Engineering Statistics (2014a).more likely than men to express a preference forwork with a clear social purpose (Diekman, Clarket al., 2011; Lubinski & Benbow, 2006; Eccles,2007). Alvarado and her colleagues hypo<strong>the</strong>sizedthat by highlighting <strong>the</strong> broad applications andsocial relevance of computer science <strong>the</strong>y couldimprove <strong>the</strong> experience for all students and raise<strong>the</strong> number of women in <strong>the</strong> major.In 2006 HMC replaced its introductory computerscience course with a course nicknamed “CSfor Scientists and Engineers,” which emphasizes<strong>the</strong> breadth of <strong>the</strong> field first and uses Python, amore flexible and forgiving programming language,ra<strong>the</strong>r than Java (Alvarado, Dodds et al., 2012).HMC professors hoped to boost students’ enjoymentand increase <strong>the</strong> number of students whochose to continue in computer science by emphasizingits diverse practical applications. In addition,<strong>the</strong> professors hoped to create an enriching environmentfor students of all experience levels whilestill cultivating <strong>the</strong> programming and computationalthinking skills required for success in futurecomputer science courses (Dodds et al., 2008).Emphasizing practical applicatio<strong>nsa</strong>nd building confidenceThe revised introductory class covers <strong>the</strong> necessaryintroductory programming skills while emphasizingpractical computer science applications asdemonstrations of <strong>the</strong> importance of computerscience to o<strong>the</strong>r fields and to society. Alvarado toldAAUW that an overemphasis on programmingin an introductory computer science course canobscure <strong>the</strong> true nature of computer science:
78SOLVING THE EQUATIONWhat we try to emphasize at Harvey Muddis that computer science is not primarilyabout programming. You are not learningto program for <strong>the</strong> sake of programming.You are learning to solve problems usingprogramming as a tool … so you have tolearn how to use <strong>the</strong> tool effectively. We tryto emphasize <strong>the</strong> actual end goal and <strong>the</strong>higher level concepts and <strong>the</strong>n tie <strong>the</strong>m backin and say, “Here’s how you use programmingto do that.”From <strong>the</strong> beginning of <strong>the</strong> course, students areexposed to <strong>the</strong> different ways in which computerscientists think about solving problems. Studentsare immediately assigned interesting programs towrite. The first assignment uses a language calledPicobot that controls a robot in a web simulation.Picobot is so easy to pick up that all students canwrite <strong>the</strong>ir own solutions to actual problems after<strong>the</strong> first class. At <strong>the</strong> same time it is a challenge foreveryone because none of <strong>the</strong> students have workedwith <strong>the</strong> language before (Alvarado, Dodds et al.,2012). Using a language that is new for everyonealso helps lessen <strong>the</strong> students’ perception that somestudents already know everything about computerprogramming while o<strong>the</strong>rs know nothing. This perceptioncan be a demotivating factor for some students,especially women, in computing (Margolis &Fisher, 2002).Alvarado emphasized that <strong>the</strong> new courseaimed to encourage those with less experience. Shetold AAUW, “I think a huge part of what discouragespeople with no experience from pursuing computerscience in college is that <strong>the</strong>y get into <strong>the</strong>seclasses, and <strong>the</strong>y feel behind from <strong>the</strong> very second<strong>the</strong>y sit in <strong>the</strong>ir seat.” She would like to see a shiftaway from <strong>the</strong> attitude that only those who canbe immediately identified as good programmersshould be encouraged to go on in computer science.Instead, she says, “If you give students <strong>the</strong> rightintroduction, <strong>the</strong> time that <strong>the</strong>y need to blossom,and <strong>the</strong> environment where <strong>the</strong>y feel comfortable,<strong>the</strong>y’re definitely capable of doing <strong>the</strong> work, and<strong>the</strong>y’re interested in doing it.”To achieve this environment HMC developedtwo tracks for its revised introductory course.Students without computer science experience areplaced in <strong>the</strong> standard section (<strong>the</strong> Gold section)and those with prior computer science backgroundare placed in <strong>the</strong> enrichment section (<strong>the</strong> Blacksection). The two sections cover <strong>the</strong> same materialat <strong>the</strong> same time, but <strong>the</strong> Black section exploresmore challenging applications of <strong>the</strong> same fundamentalconcepts (Alvarado, Dodds et al., 2012):Topic and application distinctions are carefullychosen so that <strong>the</strong> additional material isenriching and motivating but that studentsin Gold are at nei<strong>the</strong>r a real nor a perceiveddisadvantage in CS2 [<strong>the</strong> computer sciencecourse that follows <strong>the</strong> introductory course]and subsequent classes. The goal of <strong>the</strong>Black/Gold split is to give <strong>the</strong> inexperiencedstudents a comfortable and safe environmentin which to develop a passion for CSwithout interactions that might suggest—incorrectly—that <strong>the</strong>y are too “far behind”o<strong>the</strong>r students to succeed in <strong>the</strong> field. Infact, <strong>the</strong> Black/Gold split has a less touted,but equally important, benefit: In <strong>the</strong> Blacksection we help debunk some of <strong>the</strong> misconceptionsthat experienced students bringto CS, for example, that mastery of syntacticconstructs is central to <strong>the</strong> field—or that CSis merely programming.“I have taught <strong>the</strong> Gold section for a couple ofyears,” Alvarado told AAUW, “and <strong>the</strong> first day ofclass I have asked students to raise <strong>the</strong>ir hands if<strong>the</strong>y are nervous about <strong>the</strong> class. Probably about80 percent of <strong>the</strong> students raise <strong>the</strong>ir hands. Justlooking around and realizing <strong>the</strong>y are surroundedby a bunch of people who don’t know what <strong>the</strong>y aredoing ei<strong>the</strong>r seems to make students feel better.”She continued:It is fun to teach <strong>the</strong> students who havegrown up knowing that <strong>the</strong>y want to docomputer science—that’s great—but it iseven more exciting to teach <strong>the</strong> type ofstudents who have never really seen <strong>the</strong>mselvesas computer scientists and never reallythought <strong>the</strong>y could do it or have no experiencewith it and discover it. That’s how I gotinterested in women in computing—because<strong>the</strong>re tend to be a lot more women in [<strong>the</strong>latter] category.
AAUW 79HMC faculty carefully structured <strong>the</strong> revisedcourse to cover all <strong>the</strong> material required in an introductorycomputer science course in six fairly independentmodules, each representing a different waycomputer scientists think about problems. This formatallows students who might feel overwhelmedby a given topic to set aside <strong>the</strong> struggles of thatmodule before long and engage in a different set ofchallenges as <strong>the</strong> course develops. At <strong>the</strong> end of <strong>the</strong>semester, professors build on <strong>the</strong> practical knowledgestudents have gained with a team project thatincorporates programming skills and encouragescreativity (Alvarado, Dodds et al., 2012).To assist <strong>the</strong> learning process, HMC introducedoptional weekly labs, staffed by faculty, for<strong>the</strong> introductory course. As an incentive to participate,students who attend <strong>the</strong> weekly two-hourlab receive full credit for one of <strong>the</strong> three or fourweekly homework problems, regardless of whe<strong>the</strong>r<strong>the</strong>y finish <strong>the</strong> problem or not. Inexperienced studentsbenefit from a lightened workload, and morecontact with faculty encourages students to get helpearly with difficult concepts (Alvarado & Dodds,2010).Results of <strong>the</strong> redesignThe results have been overwhelmingly positive. In2007, one year after <strong>the</strong> revised course was initiallyoffered, Alvarado and her colleagues compared <strong>the</strong>later performance of students who had taken <strong>the</strong>original introductory computer science course withthose who had taken <strong>the</strong> revised course. At thattime, some of <strong>the</strong> students taking CS2 had taken<strong>the</strong> revised course in 2006, and o<strong>the</strong>rs had taken<strong>the</strong> original introductory course. Although CS2involves significant Java programming, which wastaught in <strong>the</strong> original introductory course but notin <strong>the</strong> revised course, and does not use Python, <strong>the</strong>programming language used in <strong>the</strong> new introductorycourse, midterm exam scores for students whohad taken <strong>the</strong> revised course averaged 84 percentcompared with 80 percent for students who hadtaken <strong>the</strong> original course. Likewise, students whohad taken <strong>the</strong> revised course averaged 85 percenton <strong>the</strong> CS2 final exam compared with an average of80 percent for students who had taken <strong>the</strong> originalcourse (Dodds et al., 2008). Equally important, asurvey found that 75 percent of students who took<strong>the</strong> revised course felt that it changed <strong>the</strong>ir perceptionof computer science, whereas only 47 percentof students who took <strong>the</strong> original course felt thatway (Alvarado & Dodds, 2010).Promoting early researchexperiencesThe second major change that Harvey MuddCollege introduced was <strong>the</strong> opportunity for womento participate in computer science research withina year of starting college. The goal of this changewas to instill in young women <strong>the</strong> confidence that<strong>the</strong>y can do real-world computer science. Whilephysical sciences often provide first- or second-yearstudents with <strong>the</strong> opportunity to participate in labresearch in a supporting role, until 2006 computerscience research opportunities at HMC weremostly available to students who had completedseveral computer science courses. Beginning in2006, HMC began to offer research experiences towomen <strong>the</strong> summer after <strong>the</strong>ir first year of college,before most had declared a major. Despite havingcompleted only one or two introductory computerscience courses, <strong>the</strong>se students make concrete progresson real research problems each year (Alvarado& Dodds, 2010).Accumulating evidence suggests that researchexperiences have a notable effect on encouragingwomen to pursue computer science. Among<strong>the</strong> women who participated in computer scienceresearch between 2007 and 2011, 66 percent choseto major in computer science, compared withless than 20 percent of HMC students overall(Alvarado, Dodds et al., 2012). Evidence showsthat <strong>the</strong>se research experiences have a bigger influenceon women than on men. Looking only atstudents who participated in summer computer scienceresearch after <strong>the</strong>ir first year, <strong>the</strong> survey foundthat 67 percent of female students compared with25 percent of male students listed research experienceas an influence in choosing a computer sciencemajor (Alvarado & Dodds, 2010). While <strong>the</strong>numbers of students participating in <strong>the</strong>se researchopportunities are still fairly small, <strong>the</strong> results suggestthat <strong>the</strong> opportunity to conduct computer
80SOLVING THE EQUATIONscience research early in women’s college careerscan provide important encouragement for <strong>the</strong>m topursue computer science.The Grace HopperCelebration of Womenin ComputingThe third major change that Harvey Muddinstituted was providing first-year students with<strong>the</strong> opportunity to attend—for free—<strong>the</strong> GraceHopper Celebration of Women in Computing(GHC), a conference held each fall since 1994.Named after Rear Admiral Grace Hopper, an earlycomputing pioneer who created <strong>the</strong> first compilerin 1952, GHC has grown into a ga<strong>the</strong>ring ofthousands that provides support and communityfor women in <strong>the</strong> field of computing and includesmany students. Results from <strong>the</strong> 2013 Evaluationand Impact Report (Anita Borg Institute, 2014b)indicate that students feel less isolated, morecommitted to computing, and more inspired afterattending <strong>the</strong> conference. In 2013, 85 percent of<strong>the</strong> students who responded to <strong>the</strong> survey agreedthat attending GHC increased <strong>the</strong>ir commitmentto a technology career.HMC began taking first-year female studentsto GHC in 2006. A primary objective is to recruitand retain first-year women into computing bycombating some of <strong>the</strong> factors that have beenshown to prevent women from pursuing computingcareers, including a lack of confidence, a perceptionof <strong>the</strong> culture as geeky or hostile, a misunderstandingof <strong>the</strong> field, and a lack of mentors and supportnetworks. At GHC, students see a computing culturethat is different from <strong>the</strong> stereotypical culture<strong>the</strong>y might expect. They are exposed to real-worldcomputing applications, interact with computingprofessionals, learn first-hand about excitingcomputing-related companies, and increase <strong>the</strong>irnetwork of friends and colleagues. Twelve studentsfrom HMC attended <strong>the</strong> conference in 2006, and<strong>the</strong> number has grown every year to 52 students in2014. The computer science department recruitsstudents by e-mail during <strong>the</strong> summer before <strong>the</strong>irfreshman year and takes students regardless ofwhe<strong>the</strong>r <strong>the</strong>y have expressed an interest in computerscience.Results indicate that attending GHC at a criticaljuncture in a student’s decision process (three to18 months before declaring a major) can dramaticallyaffect a student’s major and likely career path(Alvarado & Judson, 2014). Starting in 2007 andmore formally in 2009, HMC has conducted anannual survey of conference attendees to try tounderstand <strong>the</strong> effect <strong>the</strong> trip has on students.Survey results from 2009 and 2010 indicate that<strong>the</strong> conference is effective in addressing <strong>the</strong> barriersthat keep women from choosing to studycomputer science. Eighty-eight percent of studentsresponded that attending GHC gave <strong>the</strong>m a betterunderstanding of computer science, 85 percent saidGHC changed <strong>the</strong>ir perception of <strong>the</strong> computerscience culture, 72 percent indicated that GHCincreased <strong>the</strong>ir desire to take ano<strong>the</strong>r computer scienceclass, and 62 percent said that GHC increased<strong>the</strong>ir desire to major in computer science.The conference has an effect on students withsome computer science background as well asthose without. Nearly four out of five students (78percent) who were already considering majoringin computer science agreed that attending GHCincreased <strong>the</strong>ir desire to major in computer science.Among GHC attendees who were not consideringmajoring in computer science before <strong>the</strong> conference,a majority (64 percent) indicated that attending<strong>the</strong> conference increased <strong>the</strong>ir desire to takeano<strong>the</strong>r computer science course, and 43 percentsaid that attending GHC increased <strong>the</strong>ir desire tomajor in computer science (Alvarado & Judson,2014).By examining enrollment patterns, Alvaradoand her colleagues found that students whoattended GHC were indeed much more likely totake ano<strong>the</strong>r computer science course (52 percent)and major in computer science (37 percent) thanwere female students who did not attend (31 percentand 10 percent, respectively). Certainly someof this effect is due to self-selection, since thosemore interested in computer science probably moreoften applied to go to <strong>the</strong> conference. Yet lookingonly at those who came to HMC with no intentionto major in computer science, 25 percent of those
AAUW 81Figure 24. factors Contributing to HarveyMudd College Students’ Choice to Majorin Computer Science, by GenderTaking <strong>the</strong> revised introductorycomputer science courseParticipating in earlyresearch opportunitieswho attended GHC went on to major in computerscience whereas only 10 percent of those who didnot attend GHC went on to major in computerscience. This point bears repeating: One of fourwomen who came to Harvey Mudd not consideringa computer science major and who attendedGHC ended up majoring in computer science.An HMC survey found that all three changes<strong>the</strong> school instituted to increase <strong>the</strong> number ofwomen computer science majors did in fact do so,particularly <strong>the</strong> revisions made to <strong>the</strong> introductorycourse (see figure 24).Beyond Harvey MuddWomen 85%Men 81%Women 67%Men 25%Attending <strong>the</strong> Grace Hopper Celebration Women 47%Note: Percentages indicate survey respondents who participated in <strong>the</strong> practice who<strong>report</strong>ed that it contributed to <strong>the</strong>ir choice of a computer science major.Source: Alvarado, Dodds et al. (2012).Harvey Mudd’s success story is important andencouraging for o<strong>the</strong>r colleges and universitiesstruggling to diversify <strong>the</strong>ir computer sciencedepartments, but how transferable are <strong>the</strong> changes?Some school administrators might believe that thissuccess was possible only because of HMC’s smallsize and science and engineering focus. Scalabilityand logistics are major concerns for larger and morediverse colleges and universities looking to makesimilar changes.Now at <strong>the</strong> University of California, San Diego,Alvarado has first-hand experience attemptingto institute similar changes at a larger, morediverse institution. She identified <strong>the</strong> much higherstudent-to-faculty ratio at UCSD as one of <strong>the</strong> bigchallenges: “It’s more difficult to track students,it’s more difficult to schedule students, it’s moredifficult to get messaging out to students, it’s moredifficult to coordinate across <strong>the</strong>se huge sectio<strong>nsa</strong>nd across multiple offerings of <strong>the</strong> same course—<strong>the</strong> logistics are difficult.” Never<strong>the</strong>less, a small butgrowing body of evidence suggests that <strong>the</strong> changesare transferable with some modifications to fitspecific departments.Institutional commitmentTo begin with, Alvarado emphasizes <strong>the</strong> need foran institution-wide commitment to addressingwomen’s underrepresentation:The most important thing we had going forus at Harvey Mudd was that we had widesupport throughout <strong>the</strong> department andthroughout <strong>the</strong> college. We had our president,Maria Klawe, pushing for this, and shewas essential in making this work. She got inand started talking it up to people, and shefound us money and personal connections.She provided support at a very high leveland made people pay attention. I think wecan talk about <strong>the</strong> individual things we didat Harvey Mudd, and those are important,but until [increasing <strong>the</strong> representation ofwomen in computing] is made a priority ata school in a very broad way, <strong>the</strong>se initiativesmay not be successful.expanding on <strong>the</strong> HMC modelHarvey Mudd’s documented progress has recentlyprompted several companies, including Facebook,Intel, Google, and Microsoft, to give more than $1million to 15 colleges and universities to institutesimilar changes. As part of <strong>the</strong> Building Recruitingand Inclusion for Diversity initiative led by <strong>the</strong> AnitaBorg Institute for Women and Technology and HMC,researchers will ga<strong>the</strong>r data on participating institutions’progress in increasing women’s representationin computing to better understand whatkinds of changes work in which contexts (AnitaBorg Institute, 2014a).
82SOLVING THE EQUATIONRevising <strong>the</strong> introductorycomputer science courseVariations of Harvey Mudd’s revised introductorycomputer science course have now been taught at anumber of o<strong>the</strong>r colleges and universities, yieldinginsights into how well it can be adapted to o<strong>the</strong>renvironments. Bucknell University adopted <strong>the</strong>Gold approach in fall 2011, with high levels ofsatisfaction <strong>report</strong>ed among faculty and studentsdespite an increased workload and many students<strong>report</strong>ing that <strong>the</strong> course influenced <strong>the</strong>ir decisionto major in computer science (Alvarado, Doddset al., 2012). At <strong>the</strong> University of California,Riverside, <strong>the</strong> introductory course was not dividedinto sections according to <strong>the</strong> background of <strong>the</strong>students. Professors found that this negativelyaffected <strong>the</strong> experience for some students. Fromthis result Alvarado and her colleagues determinedthat maintaining <strong>the</strong> separation of students withdifferent backgrounds in computer science is anintegral part of <strong>the</strong> success of Harvey Mudd’sapproach, perhaps particularly at large, diverseinstitutions.If a full redesign is impossible, Alvarado and hercolleagues suggest that small changes to existingcourses can make a big difference. For example,teaching computer science in a contextualizedway that gives students an opportunity to see itsapplications is increasingly common at colleges anduniversities across <strong>the</strong> country. Alvarado points toGeorgia Tech, where professors developed a courseto teach computer science concepts by manipulatingdigital media, such as pictures and sounds,as a good example of a contextualized computingcurriculum with more standard introductorycontent. While science-heavy applications thatwork at HMC may not work everywhere, almostall disciplines include computation in some form.Computer science professors can construct meaningfuland relevant assignments from any numberof fields (Alvarado, Dodds et al., 2012).Early research experiencesResearch opportunities are <strong>the</strong> part of <strong>the</strong> programthat Alvarado is finding <strong>the</strong> easiest to transferto UCSD: “We have a very active set of researchprojects here at UCSD, and it’s relatively easy justto plug into <strong>the</strong> research that is going on already.”Guiding undergraduate research projects for studentswith little computer science experience mayseem daunting for faculty. Yet Alvarado and hercolleagues suggest that with <strong>the</strong> right structures inplace, it is quite doable. At Harvey Mudd, incorporatingseveral elements has led to significant satisfactionamong both students and faculty (Alvarado,Dodds et al., 2012):• Open-ended projects with sufficient scaffolding.Each project should include a well-definedsequence of tasks to accomplish, along with asubstantial creative component.• Teamwork. Students benefit from workingtoge<strong>the</strong>r. Faculty advisers must ensure thatmentor-partner relationships are natural andcomplementary and that teams work welltoge<strong>the</strong>r.• Lots of communication. Daily communication—ideallytwice a day—with faculty advisersis critical. Insist that participants maintain acareful daily log of <strong>the</strong>ir activities and questionsto make meetings with faculty more efficient.Attending <strong>the</strong> Grace HopperCelebrationHow feasible is it for o<strong>the</strong>r schools to send <strong>the</strong>irfemale computing students to Grace Hopper?According to Alvarado, <strong>the</strong> first question peopleoften ask is about funding. HMC has been pleasantlysurprised at its ability to raise internal andexternal funding to send all students who haveapplied to GHC because of a high interest frompotential employers, alumni, and philanthropists.Demonstrated results should assist in fur<strong>the</strong>r fundraisingfor HMC and o<strong>the</strong>r schools (Alvarado &Judson, 2014).Even if colleges and universities can raise fundsto send students to GHC, size limitations of GHCcould also be an issue. While GHC continues togrow, it also sells out every year. One answer to thisproblem is more celebrations. Regional conferences,hosted by a partnership of <strong>the</strong> Anita Borg Institutefor Women and Technology, <strong>the</strong> Association forComputing Machinery Women’s Council, and<strong>the</strong> National Center for Women and Information
AAUW 83Technology, are springing up around <strong>the</strong> UnitedStates and around <strong>the</strong> world. Alvarado has focusedon <strong>the</strong> regional Grace Hopper Celebrations for<strong>the</strong> students at UCSD because <strong>the</strong> conferences areeasier (and less expensive) to reach and are oftenfree for students. Alvarado said:Regional celebrations can be better because<strong>the</strong>y are not as big and intimidating. Eightthousand people attended <strong>the</strong> most recentnational Grace Hopper Celebration, andfirst-year students can feel kind of lost.Regional celebrations are much moreintimate—around 200 people—and for <strong>the</strong>most part mainly students attend.What can we do?Since 2006, when it first implemented <strong>the</strong> practicessummarized above, Harvey Mudd College has seena dramatic and lasting increase in <strong>the</strong> number andpercentage of women who choose to major in computerscience. Clearly <strong>the</strong> turning point in women’srepresentation occurred with <strong>the</strong> class of 2010 (seefigure 23), <strong>the</strong> first class that experienced <strong>the</strong> newpractices (Alvarado, Dodds et al., 2012).HMC hasn’t solved all <strong>the</strong> problems, however.When students who had considered majoringin computer science but decided against it wereasked to list important factors in <strong>the</strong>ir decision, 28percent of women and 31 percent of men listed“I didn’t feel like I fit in as a CS major/I didn’tfeel comfortable with <strong>the</strong> culture” as an importantfactor. In addition Alvarado and her colleaguesfound that more women (26 percent) than men(14 percent) chose a different major in part because<strong>the</strong>y thought that computer science was too hard(Alvarado & Dodds, 2010).Still, HMC’s experiment is encouraging, notleast because <strong>the</strong> school has also seen a significantincrease in <strong>the</strong> number of men majoring incomputer science in <strong>the</strong> past few years. From dataHMC has collected, it appears that as many menas women enjoy and are motivated by <strong>the</strong> revisedintroductory course. When HMC asked <strong>the</strong>graduating classes from 2009 to 2012, “What is <strong>the</strong>SINGLE most important experience that led youto choose a CS major?” <strong>the</strong> revised course was <strong>the</strong>most cited experience for women (34 percent) andtied for <strong>the</strong> most cited experience for men (29 percent)with “experiences before college” (Alvarado,Dodds et al., 2012).Recommendations that stem from HarveyMudd’s success include exposing a broad range ofpeople to computing and moving away from <strong>the</strong>idea that certain people (often with strong programmingskills) are cut out for computer sciencewhile o<strong>the</strong>rs are not. High schools, middle schools,and elementary schools should offer courses incomputer science. The more experiences studentshave with computer science before <strong>the</strong>y get to college,<strong>the</strong> more opportunities will be open to <strong>the</strong>m.Colleges and universities should require allundergraduate students to take at least one computerscience course, no matter what <strong>the</strong>ir major,and should engage students in hands-on researchearly in <strong>the</strong>ir college education. College and universitycomputer science departments should highlightas early as possible <strong>the</strong> different facets that makeup computer science and show <strong>the</strong> impact thatcomputer science has on society.Departments should split classes by experience,providing students with less experience in computerscience with <strong>the</strong> time and environment <strong>the</strong>yneed to build <strong>the</strong>ir skills and interest. Even withoutchanging an introductory computer science course,it may be possible to develop at very little costmultiple sections to accommodate students withdifferent experience levels.Computer science departments should take studentsto <strong>the</strong> Grace Hopper Celebration of Womenin Computing or similar conferences to give <strong>the</strong>m asense of identity with people in <strong>the</strong> field and share<strong>the</strong>ir excitement for it. Taking even a few studentscan change <strong>the</strong>ir mindset and have an importanteffect on a school’s program. Schools should take amix of students interested in computing and thosenot considering computer science as a major.
Chapter 8.Persistence andSense of FitWomen, after a few weeks or months or years inengineering, might begin to see <strong>the</strong>mselves as lesscapable professionals than men because <strong>the</strong>y areinteracting in a space that tends to be masculine andtends to devalue what are seen as feminine traits orfeminine contributions to <strong>the</strong> field.—Erin Cech
86SOLVING THE EQUATION“Being an engineer” includes more than knowledgeand skills in ma<strong>the</strong>matics and science. Prospectiveengineers must be able to deal with ambiguous,messy problems, and <strong>the</strong>y need to share a commitmentto <strong>the</strong> values of <strong>the</strong> profession and becomfortable with its norms. Erin Cech, an assistantprofessor of sociology at Rice University, andher colleagues Brian Rubineau, Susan Silbey, andCarroll Seron noticed that not much researchaddressed “not only <strong>the</strong> confidence a person hasin his/her ability to do things like take a math testor take a science test but also <strong>the</strong> confidence toimagine oneself going out and applying for a jobin engineering and being an engineer.” They coined<strong>the</strong> term “professional role confidence” to describehow comfortable professionals or prospective professionalsare in <strong>the</strong>ir ability to fit into and fulfill all<strong>the</strong> aspects of <strong>the</strong>ir roles. Cech and her colleaguesmeasured <strong>the</strong> influence of this sense of fit on <strong>the</strong>persistence of women and men in engineeringprograms in colleges and universities.Expertise and careerfitconfidence amongundergraduatesIn engineering, a field in which <strong>the</strong> educationaland professional environments are closely linked,professional role confidence starts to develop aswomen and men begin <strong>the</strong>ir engineering education.Undergraduate engineering students are engineersin-training,adding direct experiences with <strong>the</strong> professionto <strong>the</strong>ir previous understanding of <strong>the</strong> field.As <strong>the</strong>y train for <strong>the</strong>ir careers, engineering studentsconsider whe<strong>the</strong>r <strong>the</strong>y will be successful, happyprofessionals in <strong>the</strong>ir chosen field. Whe<strong>the</strong>r or not<strong>the</strong>y see <strong>the</strong>mselves as successful and fulfilled andwhe<strong>the</strong>r <strong>the</strong>y feel that engineering is a good fit for<strong>the</strong>ir skills and interests will affect whe<strong>the</strong>r <strong>the</strong>ycontinue working toward a degree and whe<strong>the</strong>r<strong>the</strong>y pursue an engineering career.Cech and her colleagues (2011) fur<strong>the</strong>r refined<strong>the</strong> concept of professional role confidence bydividing it into two discrete concepts: expertiseconfidence (<strong>the</strong> confidence that one possesses<strong>the</strong> requisite skills and knowledge to be a professionalin a chosen field) and career-fit confidence(confidence that <strong>the</strong> field is consistent with one’sinterests, values, and identity). Expertise confidenceanswers <strong>the</strong> question, “Do I have <strong>the</strong> capabilitiesto be successful in this role?” Career-fit confidenceanswers <strong>the</strong> question, “Is this <strong>the</strong> right career forme long term?”To understand gender differences in professionalrole confidence and <strong>the</strong> impact of professionalrole confidence on retention in engineering,Cech and her colleagues interviewed 288 engineeringstudents in <strong>the</strong> second semester of <strong>the</strong>ir firstyear and again in <strong>the</strong>ir fourth year of college. Thestudents studied at four universities: a land-grantcollege typical of <strong>the</strong> public institutions that educate80 percent of U.S. engineers, a highly rankedprivate engineering school, and two small, innovativeprograms developed to challenge <strong>the</strong> engineeringeducation offered at conventional engineeringschools.During <strong>the</strong>ir first semester, students at all fourschools were required to take a course intended tointroduce <strong>the</strong>m to <strong>the</strong> engineering profession. Asdescribed by Cech (2014, p. 49), students quicklydevelop a sense of <strong>the</strong> culture and norms once <strong>the</strong>yenter <strong>the</strong>ir training program:Through classes, internships, design projects,and friendships, students are transformedfrom laypersons into engineers; <strong>the</strong>y areexpected to adopt <strong>the</strong> profession’s epistemologies,values, and norms; identify withparticular symbols; and learn to project aconfident, capable image of expertise.Cech and her colleagues assessed first-yearengineering students’ expertise confidence by asking<strong>the</strong>m to rate <strong>the</strong>ir confidence on <strong>the</strong> followingthree indicators as a result of <strong>the</strong>ir engineeringcourses:• Developing useful skills• Advancing to <strong>the</strong> next level in engineering• Believing that I have <strong>the</strong> ability to be successfulin my careerThe researchers assessed career-fit confidenceby asking students in <strong>the</strong>ir first year of engineeringschool to rate <strong>the</strong>ir confidence in <strong>the</strong> following fourindicators as a result of <strong>the</strong>ir engineering courses:
AAUW 87Measuring professionalrole confidence earlyAssessing students’ professional role confidenceduring <strong>the</strong>ir first year of engineering school mayseem early. How confident can young people feelabout <strong>the</strong>ir engineering expertise and about howwell an engineering career is likely to fit <strong>the</strong>m lessthan a year into <strong>the</strong>ir training? In an interview withAAUW, Cech explained why it makes sense to assessprofessional role confidence so soon:Students come in with a fairly vagueunderstanding of what this occupationalfield is. They often get information about<strong>the</strong> field from websites or friends or relativeswho are engineers. It’s not until <strong>the</strong>ystart taking classes in <strong>the</strong> major and startmeeting o<strong>the</strong>r members of <strong>the</strong>ir major that<strong>the</strong>y start <strong>the</strong>ir professional socializationas engineers. And <strong>the</strong> socialization processis quite intense. The undergraduate experienceis not only about learning how to do<strong>the</strong> homework assignments but learningwhat it means to be an engineer. So eventhough we take this measure in <strong>the</strong> springsemester of <strong>the</strong> freshman year, <strong>the</strong>y’vealready encountered a ra<strong>the</strong>r intensesocialization process where <strong>the</strong>y’re tryingto figure out—talking with <strong>the</strong>ir classmates,talking with professors—what itmeans to be part of this profession andwhe<strong>the</strong>r it is <strong>the</strong> right fit for <strong>the</strong>m.• Believing that engineering is <strong>the</strong> right professionfor me• Selecting <strong>the</strong> right field of engineering for me• Finding a satisfying job• Feeling a commitment to engineeringFor <strong>the</strong>ir persistence measures, Cech and hercolleagues assessed two kinds of persistence: behavioralpersistence (students’ actual completion ofan engineering major between <strong>the</strong> first and fourthyears) and intentional persistence (students’ beliefin <strong>the</strong> fourth year that <strong>the</strong>y will be an engineer infive years).Staying with <strong>the</strong> programCech and her colleagues (2011) found that menexpressed more behavioral and intentional persistencethan women did. That is, men were morelikely than women to persist from <strong>the</strong> first to <strong>the</strong>fourth year and actually earn an engineering degree,and in <strong>the</strong>ir senior year men were more likely thanwomen to <strong>report</strong> that <strong>the</strong>y intended to be an engineerin five years.In addition, men expressed significantly moreprofessional role confidence, both expertise confidenceand career-fit confidence, than women did,even after controlling for students’ actual performance.Consistent with <strong>the</strong> researchers’ hypo<strong>the</strong>sis,students with greater confidence in <strong>the</strong>ir expertiseand career fit were more likely to persist in engineering.Most important, when women and menhad <strong>the</strong> same expertise confidence and career-fitconfidence, women were no less likely to persistthan men—ei<strong>the</strong>r in completing an engineeringdegree (behavioral persistence) or in intendingto work as an engineer in <strong>the</strong> future (intentionalpersistence). In o<strong>the</strong>r words, women and men withsimilar views on whe<strong>the</strong>r engineering was a goodfit for <strong>the</strong>ir skills, interests, and values were equallylikely to continue in engineering in school andintend to continue in engineering in <strong>the</strong> workforce.The study also assessed <strong>the</strong> influence of familyplans and self-assessment of ma<strong>the</strong>matical abilitybut did not find evidence that ei<strong>the</strong>r of <strong>the</strong>se measureslowered women’s persistence in engineeringduring engineering school.Looking at <strong>the</strong> different measures of persistence,Cech and her colleagues found that expertiseconfidence predicted behavioral persistence andthat career-fit confidence predicted intentional persistence.In o<strong>the</strong>r words, students who believed that<strong>the</strong>ir skills and expertise fit <strong>the</strong> engineering fieldwere more likely to persist in <strong>the</strong> major and earnan engineering degree. On <strong>the</strong> o<strong>the</strong>r hand, studentswho felt that <strong>the</strong>ir interests and values fit <strong>the</strong>
88SOLVING THE EQUATIONculture of engineering were more likely to intend tobe an engineer five years after graduation.The finding that expertise confidence has littleimpact on plans for a future career in engineeringwhile career-fit confidence is strongly and positivelyrelated to future career plans is particularlyimportant. The degree to which students expect tofit in with <strong>the</strong> engineering culture and norms is astrong indication of <strong>the</strong>ir intention to pursue anengineering career, much more so than students’confidence in <strong>the</strong>ir engineering skills.A gendered sense of fitNaturally, engineering students expect that <strong>the</strong>skills taught in engineering courses will be <strong>the</strong>skills <strong>the</strong>y will use in <strong>the</strong> field. But Cech arguesthat engineering degree programs largely ignore avariety of areas of expertise that are necessary to besuccessful as a professional engineer:The classes that students take are overwhelminglymath- and science-based, withperhaps one class on technical writingand one class on engineering ethics. But ifyou talk to engineers who are in <strong>the</strong> field,<strong>the</strong>y will tell you <strong>the</strong>y are required to havea whole fleet of o<strong>the</strong>r kinds of skills to besuccessful. They need to be good communicators,<strong>the</strong>y need to be good managers,<strong>the</strong>y need to be organized, and <strong>the</strong>y need tounderstand <strong>the</strong> complexities of <strong>the</strong> relationshipbetween <strong>the</strong> technical things <strong>the</strong>y’reworking on and o<strong>the</strong>r social processes.A narrow math and science emphasis disproportionatelydisadvantages women because itemphasizes male-stereotyped skills while devaluingskills that are gender neutral or female-stereotyped,such as writing, communication, and managerialskills. Cech recommends displaying <strong>the</strong> need for<strong>the</strong>se o<strong>the</strong>r competencies by providing opportunitiesfor undergraduates to do actual engineeringand design work as soon as possible. Working onengineering problems in <strong>the</strong> field, Cech says, differsfrom solving homework problems, and she wouldlike to see undergraduates “have exposure to what<strong>the</strong> profession of engineering actually is, ra<strong>the</strong>rthan what <strong>the</strong>y imagine <strong>the</strong> engineering professionto be.” Engineering students may be developinga sense that <strong>the</strong> engineering profession is not <strong>the</strong>right fit for <strong>the</strong>m based on mistaken or incompleteperceptions of what <strong>the</strong> work is actually like.As described in chapter 6, a second reason that<strong>the</strong> undergraduate engineering experience may beturning women away from engineering is a lackof emphasis on <strong>the</strong> social impact of engineeringwork. Cech (2014) found that students’ interest inpublic welfare concerns declined over <strong>the</strong> course of<strong>the</strong> undergraduate engineering program. She toldAAUW:The professional socialization that [studentsare] getting in <strong>the</strong>se engineering programsis generally not emphasizing things likepublic welfare issues and social consciousness.This relates to <strong>the</strong> broader issue of howwe represent <strong>the</strong> engineering profession atits fullest level of complexity, not characterizingit as only math and science equationsbut articulating <strong>the</strong> variety of things it hasto offer. Portraying engineering in this morecomplex way would allow a broader spectrumof students to believe that engineeringis <strong>the</strong> right field for <strong>the</strong>m, to have greatercareer-fit confidence and greater expertiseconfidence. More students would likely seeengineering as something that <strong>the</strong>y could begood at. There are very deeply rooted needsfor consideration of social justice and publicwelfare concerns in <strong>the</strong> kind of work thatengineers do, and separating <strong>the</strong>se kinds ofquestions from <strong>the</strong> standard curriculum isproblematic.Fitting in at workCech’s study specifically measures <strong>the</strong> professionalrole confidence of undergraduates. Yet from allappearances, <strong>the</strong> issue of women leaving <strong>the</strong> engineeringworkforce is more significant than <strong>the</strong> issueof women leaving college engineering programs.Cech told AAUW:There is nothing to suggest that <strong>the</strong>se processesend after people walk across <strong>the</strong> stageand have <strong>the</strong>ir degree. We don’t leave ourprofessional training and go into <strong>the</strong> workforceand never again worry about how we’re
AAUW 89doing and whe<strong>the</strong>r or not we have <strong>the</strong> rightexpertise or whe<strong>the</strong>r <strong>the</strong> profession is <strong>the</strong>right fit. These are questions that continuallycome up for young professionals and perhapsprofessionals throughout <strong>the</strong>ir career.Cech and her colleagues are following up with<strong>the</strong> students who participated in <strong>the</strong> study to seehow professional role confidence in college relatesto persistence in <strong>the</strong> engineering workforce.Research on <strong>the</strong> influence of professional roleconfidence in <strong>the</strong> engineering field is in its earlystages. The research to date demonstrates thatprofessional role confidence is significantly associatedwith engineering persistence and that womentend to have less professional confidence than menhave. Development of an identity as an engineerhas been shown to be a characteristic of womenwho persist in engineering careers (Buse et al.,2013). These findings point to increasing women’ssense of fit with <strong>the</strong> professional role as a potentialkey to closing gender gaps in engineering. Whenasked what can be done to increase professionalrole confidence among women in engineering,Cech acknowledged that it is difficult: “It requireschanging <strong>the</strong> culture of <strong>the</strong> field, <strong>the</strong> culture of<strong>the</strong> profession.” Increasing <strong>the</strong> number of womenwho are comfortable being an engineer likely willrequire sustained effort from engineering schoolsand workplaces to make clear <strong>the</strong> reasons whywomen belong in engineering.What can we do?A number of recommendations stem from thisresearch. College and university engineeringdepartments should emphasize <strong>the</strong> wide variety ofexpertise necessary to be successful as an engineer.A narrow focus on math and science obscures <strong>the</strong>o<strong>the</strong>r areas of expertise—writing, communicating,organizing, and managing—that engineers needto be successful. Including engineering designactivities in <strong>the</strong> field early in undergraduate courseworkallows students to see <strong>the</strong> differences betweentextbook problems and <strong>the</strong> creativity and criticalthinking necessary for actual engineering problemsolving. Recognizing that <strong>the</strong>se areas of expertiseare critical to <strong>the</strong> engineering role also shifts <strong>the</strong>image of who is a good fit for engineering andstops <strong>the</strong> devaluing of competencies and contributionsthat are female stereotyped.Enable early contact between students andprofessionals. Meaningful contact with engineersin <strong>the</strong> field provides students with role models andmentors and also helps students understand <strong>the</strong>breadth of skills that <strong>the</strong>y will need to be successful.Individuals with low professional role confidencecould benefit from interaction with professionalswith whom <strong>the</strong>y can identify.Provide girls with opportunities to tinker. Moreboys than girls arrive at college with experiencetinkering or programming. Giving girls opportunitiesto develop confidence in <strong>the</strong>ir design abilitieswell before <strong>the</strong>y finish high school could help <strong>the</strong>mdevelop expertise confidence and comfort withbeing an engineer, which could <strong>the</strong>n increase <strong>the</strong>irpersistence in <strong>the</strong> field. Increasing <strong>the</strong> number ofgirls who enter college with experience in designcould also help shift <strong>the</strong> perception that women arenot naturally a good fit for engineering work.Finally, spread <strong>the</strong> word that engineering skillsand competencies are learned, not innate (Dweck,2007). A conception that some people’s brains arehardwired to do engineering work (and that menare better at math and science than women are)contributes to low professional role confidence byperpetuating a stereotype that some people arenatural engineers while o<strong>the</strong>rs are a poor fit forengineering. In engineering classrooms, reinforcing<strong>the</strong> idea that successful engineers are those willingto practice to develop <strong>the</strong>ir skills and persistthrough difficulties can help reduce false assumptio<strong>nsa</strong>bout engineering competence.
Chapter 9.A Workplacefor EveryoneA lot of <strong>the</strong> studies have focused on fixing women—fixing<strong>the</strong>ir confidence, fixing <strong>the</strong>ir interests. We did not findthat any of those factors influenced women engineers’persistence decisions at all, which is why we are sayingwe really need to be focusing on <strong>the</strong> environment.—Nadya Fouad
92SOLVING THE EQUATIONMany researchers have looked at why womenchoose to enter STEM careers such as engineeringand computing and <strong>the</strong> factors involved inpreparing women for <strong>the</strong>se careers, but retention ofwomen in <strong>the</strong>se fields has received much less attention.Recruiting women will be truly successful onlyif women who start in engineering and computingstay in <strong>the</strong>se fields. Despite evidence that womenare more likely than men to leave engineering andtechnical jobs (Hunt, 2010; Society of WomenEngineers, 2006; Hewlett, Buck Luce et al., 2008),few researchers have studied why, and under whatconditions, women choose to leave.A survey of womenengineersIn 2009, distinguished professor of educational psychologyNadya Fouad and business school professorRomila Singh at <strong>the</strong> University of Wisconsin,Milwaukee, along with colleagues Mary Fitzpatrickand Jane Liu, launched a survey funded by <strong>the</strong>National Science Foundation to uncover <strong>the</strong> factorsthat lead women to stay in engineering. Drawingfrom <strong>the</strong> population of women who had graduatedwith an undergraduate degree in engineering atany time in <strong>the</strong> past (Fouad et al., 2012), <strong>the</strong> surveycompared women currently working as engineerswith women who had left <strong>the</strong> field. Participantsincluded women who earned engineering degreesas far back as 1947 and as recently as 2010.The researchers were determined to reach asrepresentative a sample as possible, so <strong>the</strong>y contactedengineering deans at <strong>the</strong> top 50 universitiesthat graduate women in engineering. To understandissues particular to women of color in engineering,<strong>the</strong> researchers also contacted <strong>the</strong> top 20universities that graduate Latino, black, and Asianengineers. Thirty universities agreed to participate,including universities in every region of <strong>the</strong> UnitedStates: private institutions such as Cornell andStanford; public universities such as Penn State and<strong>the</strong> University of Florida; and technical schoolssuch as MIT and Georgia Tech.In addition to <strong>the</strong> women at <strong>the</strong> 30 official partneruniversities, women from ano<strong>the</strong>r 200 universitiescompleted <strong>the</strong> survey (at NSFpower.org) afterhearing about it from o<strong>the</strong>rs (Singh et al., 2013). In<strong>the</strong> end, <strong>the</strong> researchers received more than 5,500responses.The survey was designed to assess factors thatcould influence engineers’ likelihood of leaving <strong>the</strong>field, such as vocational interests, job and careersatisfaction, work-family conflict, training anddevelopment opportunities, a variety of workplacesupport mechanisms and initiatives, underminingbehavior in <strong>the</strong> work environment, and surveytakers’ commitment to <strong>the</strong>ir employer and <strong>the</strong>engineering profession. The survey also includedquestions that allowed <strong>the</strong> researchers to assessrespondents’ self-efficacy (<strong>the</strong>ir belief or confidencein <strong>the</strong>ir ability to complete tasks or reach goals) andpositive outcome expectations (<strong>the</strong>ir belief or confidencethat a given behavior will lead to a particularoutcome).Fouad, Singh, and <strong>the</strong>ir colleagues publisheda <strong>report</strong> in 2012 based on <strong>the</strong> more than 5,500survey responses from women with engineeringdegrees. 1 More than half of <strong>the</strong> respondents werecurrently working as engineers, about a quarter hadpreviously worked as engineers but had left <strong>the</strong>field, and <strong>the</strong> rest had earned engineering degreesbut never worked as engineers.Those who stayed versus thosewho leftTo understand any possible differences betweenwomen who had left and women who had stayedin engineering, <strong>the</strong> researchers compared <strong>the</strong>survey responses of women currently working inengineering with <strong>the</strong> responses of women who hadleft within <strong>the</strong> previous five years. 2 By a surprisingnumber of measures, <strong>the</strong> two groups of womenwere similar: They had similar undergraduatemajors, were equally likely to be married and havechildren, were of similar racial/ethnic backgrounds,and were of similar age.The two groups of women were also psychologicallysimilar, being equally interested in engineeringand equally confident in <strong>the</strong>ir engineering abilities.Both groups were equally confident in <strong>the</strong>ir abilityto navigate <strong>the</strong> political environment in <strong>the</strong>ir workplaceand <strong>the</strong>ir ability to manage multiple work-life
AAUW 93role demands and had similar expectations in <strong>the</strong>searenas.What was not <strong>the</strong> same for <strong>the</strong> two groupsof engineers was <strong>the</strong> workplace environment.Compared with those who stayed in engineering,those who left were• Less likely to <strong>report</strong> opportunities for trainingand development that would have helped <strong>the</strong>madvance• Less likely to <strong>report</strong> support from a supervisoror co-worker• More likely to <strong>report</strong> undermining behaviorsfrom supervisors• Less likely to <strong>report</strong> support for balancing workand nonwork rolesIndeed, <strong>the</strong> survey clearly showed that womenare not leaving because of something <strong>the</strong>y arelacking. As Fouad told AAUW, “A lot of <strong>the</strong>studies have focused on fixing women—fixing<strong>the</strong>ir confidence, fixing <strong>the</strong>ir interests. We did notfind that any of those factors influenced womenengineers’ persistence decisions at all, which is whywe are saying we really need to be focusing on <strong>the</strong>environment.”Workplace barriersComparing persisters with nonpersisters is usefulfor understanding which factors contribute towomen leaving engineering. To better understand<strong>the</strong> differences between women who are solidlycommitted to <strong>the</strong>ir engineering jobs and those whomay leave, <strong>the</strong> researchers analyzed how currentengineers’ job satisfaction and intention to leave<strong>the</strong>ir organizations related to a number of o<strong>the</strong>rfactors. They found two workplace factors thatlowered job satisfaction: excessive and ill-definedwork goals and various kinds of incivility, includingexplicit insults directed at women.Excessive workloads included being expectedto work more than 50 hours per week and takework home at night and on weekends, as well astoo much responsibility without commensurateresources such as budget, staff, and time. Illdefinedresponsibilities included a lack of clearlydefined goals, objectives, and responsibilities andcontradictory and conflicting work requests andrequirements. Engineers burdened with excessiveand ill-defined work goals were <strong>the</strong> least satisfiedwith <strong>the</strong>ir jobs and <strong>the</strong> most inclined to leave <strong>the</strong>irorganizations. Overwork and lack of clarity aboutassignments were related not only to a weakercommitment to <strong>the</strong>ir specific employer but to <strong>the</strong>engineering profession as a whole.Incivility at workThe second factor that lowered women engineers’job satisfaction, incivility at work, included beingtreated in a condescending, patronizing, or discourteousmanner by supervisors, senior managers,and co-workers. The researchers asked participantsto estimate on a scale of 1 (never) to 6 (every day)how frequently in <strong>the</strong> past month <strong>the</strong>y had experiencedspecific undermining behaviors (from supervisorsand co-workers separately), such as having<strong>the</strong>ir ideas belittled; being insulted, talked downto, not defended, or given <strong>the</strong> silent treatment; andgenerally having <strong>the</strong>ir efforts to be successful on<strong>the</strong> job undercut. In addition, participants wereasked how often in <strong>the</strong> past year <strong>the</strong>y had observedany supervisor, senior manager, or colleague exhibitsexist behaviors such as ignoring or interrupting afemale employee, speaking in a condescending orpatronizing manner to a female employee, makingoffensive or embarrassing public comments about<strong>the</strong> physical appearance of a female employee, ormaking sexually suggestive comments to a femaleemployee.Figure 25 shows that, on average, engineerswho <strong>report</strong>ed less satisfaction with <strong>the</strong>ir jobs also<strong>report</strong>ed observing more sexist behavior at work in<strong>the</strong> last year and experiencing more underminingbehaviors by both co-workers and supervisors in<strong>the</strong> last month. While <strong>the</strong> differences are not huge,<strong>the</strong>y are statistically significant, and, of course, noone should have to endure <strong>the</strong>se types of behaviorsin <strong>the</strong> workplace or elsewhere, so <strong>the</strong> answer to allthree of <strong>the</strong>se questions should be never.Female engineers who <strong>report</strong>ed that supervisorsmore frequently belittled, patronized, or systematicallyundermined <strong>the</strong>m were <strong>the</strong> least satisfied with<strong>the</strong>ir jobs and were less satisfied than those receivinguncivil treatment from co-workers. Womenin such undermining environments were also less
94SOLVING THE EQUATIONMean frequencyFIGURE 25. FEMALE ENGINEERS' EXPERIENCEOF INCIVILITY AT WORK, BY LEVEL OFJOB SATISFACTION2.01.81.61.41.21.0Observed sexistbehaviorExperiencedunderminingbehaviorsby supervisorEngineers withlow job satisfactionEngineers withhigh job satisfactionExperiencedunderminingbehaviorsby co-workersNote: Scale from 1 (never) to 6 (every day). Participants estimated <strong>the</strong> frequency withwhich <strong>the</strong>y experienced undermining behaviors in <strong>the</strong> past month and <strong>the</strong> frequencywith which <strong>the</strong>y observed sexist behavior in <strong>the</strong> past year.Source: Fouad et al. (2012). AAUW communication with Romila Singh.committed to <strong>the</strong> organization as a whole, morelikely to intend to leave <strong>the</strong>ir job, and more likely toexperience high levels of work-family conflict.These findings are particularly revealing because,although some research has identified <strong>the</strong> engineeringwork culture as unfriendly to women, this studyis <strong>the</strong> first to identify specific kinds of underminingbehaviors that may contribute to an uncomfortablework climate for women and affect <strong>the</strong>ir inclinationto leave <strong>the</strong> field of engineering.Why women stay in engineeringAccording to Fouad, Singh, and <strong>the</strong>ir colleagues,current engineers who were <strong>the</strong> most satisfied with<strong>the</strong>ir jobs and committed to <strong>the</strong> field of engineeringoverall were <strong>the</strong> most likely to experiencesupport in <strong>the</strong> workplace. Engineers who weresatisfied with <strong>the</strong>ir jobs felt that <strong>the</strong>ir contributionswere valued and recognized and that <strong>the</strong>irorganization cared about <strong>the</strong>ir well-being, opinions,and general satisfaction at work. They also receivedmore tangible development opportunities, such aschallenging assignments that helped <strong>the</strong>m advance<strong>the</strong>ir skills and formal training opportunities.They worked for organizations that provided clear,transparent paths for advancement and were morelikely than engineers with low job satisfaction to<strong>report</strong> that <strong>the</strong>ir organization provided supportivework-life policies.Self-efficacy and positiveoutcome expectationsDelving a bit deeper, Singh, Fouad, and <strong>the</strong>ircolleagues Mary Fitzpatrick, Jane Liu, KevinCappaert, and Catia Figuereido conducted afollow-up analysis (Singh et al., 2013) of twofactors that <strong>the</strong>y suspected might be especiallyimportant for understanding current female engineers’intentions to stay or leave: engineering taskself-efficacy (<strong>the</strong> strength of a person’s belief in herability to complete tasks and reach goals related toher engineering job) and engineering task outcomeexpectations (<strong>the</strong> strength of a person’s belief that acertain action or behavior related to her engineeringwork will result in a positive outcome).To estimate current engineers’ levels of engineeringtask self-efficacy, <strong>the</strong> researchers lookedat <strong>the</strong> extent of study participants’ confidence on ascale of 1 (not at all confident) to 5 (very confident)in performing a variety of common engineeringtasks, such as “design a new product or project tomeet specified requirements” and “troubleshoota failure of a technical component or system.” Toestimate current engineers’ levels of outcome expectations,<strong>the</strong> researchers analyzed participant ratingsof <strong>the</strong>ir level of agreement, from 1 (strongly disagree)to 5 (strongly agree), with statements such as
AAUW 95“If I perform my job tasks well, <strong>the</strong>n I will earn <strong>the</strong>respect of my co-workers,” “If I achieve in my job,I expect I’ll receive good raises,” and “When I amsuccessful at my work tasks, <strong>the</strong>n my manager(s)will be impressed.”Analysis of <strong>the</strong> survey responses showed thatengineering task self-efficacy and positive outcomeexpectations were related both to higher jobsatisfaction and higher organizational commitment,which in turn were related to engineers’ greaterintention to stay in <strong>the</strong>ir jobs.What about men?Some have asked whe<strong>the</strong>r women and menrespond differently when it comes to workplaceenvironment—including self-efficacy and positiveoutcome expectations—and intentions to stay orleave engineering. Singh and Fouad responded bylaunching a similar study for men (at NSFgears.org). Preliminary results suggest that <strong>the</strong> samethings that affect women’s tendency to leave organizations—lackof opportunities for advancement,lack of opportunities for training and development,and excessive workloads—also affect men. SaysFouad, “We’re arguing that changing <strong>the</strong> environmentis good for everybody, men and women.”A better future for women (andmen) in engineering?Singh and Fouad believe that creating supportivework environments is possible. Singh told AAUW:A lot of <strong>the</strong> recommended changes thatcome out of our study have been put in placeby organizations often cited in lists of greatplaces to work. The recommendations thatfollow from our findings are not necessarilynovel or foreign. It’s just that some engineeringfirms may have been slow to acknowledge<strong>the</strong> need for <strong>the</strong>m. … What we reallyneed are systemic changes, an overhaul of<strong>the</strong> entire system.While not necessarily easy to change, reducingexcessive work demands and clarifying work goalsseem like possible changes for leaders of engineeringorganizations to make. But is an uncivil andundermining work environment changeable? Singhthinks so:There’s a lot of research on incivility andundermining behaviors in organizations.Organizations that have taken a very intentionaland purposeful approach to combatingthat have succeeded. These are all learnedbehaviors that, if <strong>the</strong>y’re not dealt with andnegative consequences don’t follow, employeescome to understand that <strong>the</strong>y can getaway with. The prevalence of incivility andundermining behaviors in an organizationreally stems from <strong>the</strong> organizational culture:whe<strong>the</strong>r <strong>the</strong> top leadership and whe<strong>the</strong>r <strong>the</strong>leadership at every level is tolerant or intolerantof <strong>the</strong>se kinds of behaviors.What can organizationsdo?Self-efficacy and positive outcome expectationsmight at first glance appear to be individuallydeveloped and determined, but both characteristicsare influenced to a great degree by an individual’senvironment. “[Self-efficacy is] not somethingthat is fixed,” explains Singh. “It’s really malleable.It is context sensitive and can change in responseto certain elements in <strong>the</strong> work environment.” Sowhile some components of an individual’s engineeringtask self-efficacy and positive outcomeexpectations are specific to an individual’s psychologicalmakeup, both characteristics are decidedlyaffected by an individual’s workplace environment.For example, a person at one company might begiven <strong>the</strong> resources and authority to troubleshoot afailure and be rewarded if she solves <strong>the</strong> problem.That person is likely to have high self-efficacy andpositive outcome expectations. At ano<strong>the</strong>r company<strong>the</strong> same person might not be provided with <strong>the</strong>resources and authority to investigate <strong>the</strong> failureand will not be rewarded if she solves <strong>the</strong> problem.In <strong>the</strong> second case <strong>the</strong> person will likely have littleconfidence in her ability to succeed and will likelydevelop negative outcome expectations.Organizations can improve employees’ selfefficacyand outcome expectations through anumber of actions and policies. Organizations can
96SOLVING THE EQUATIONensure that employee roles and responsibilities areclearly defined, employees are provided with <strong>the</strong>resources <strong>the</strong>y need to fulfill those responsibilities,and employees receive training and development.Organizations can acknowledge and rewardemployee contributions and ensure that employeesdo not have excessive workloads. Through <strong>the</strong>seefforts, organizations can improve self-efficacy andpositive outcome expectations among all employeesand in <strong>the</strong> process retain <strong>the</strong>ir female engineers.Chapter 9 notes1. Stemming <strong>the</strong> Tide (Fouad et al., 2012) was written for and funded by <strong>the</strong> National Science Foundation. Unlike <strong>the</strong> follow-uparticle (Singh et al., 2013), <strong>the</strong> 2012 <strong>report</strong> was not peer-reviewed. Despite that fact, <strong>the</strong> findings from <strong>the</strong> 2012 <strong>report</strong> areincluded here because of <strong>the</strong> importance of understanding factors that relate to <strong>the</strong> retention of women in engineering to <strong>the</strong>overall issue of women’s underrepresentation in engineering and computing. Fouad, Singh, and <strong>the</strong>ir colleagues’ study (2012) is<strong>the</strong> first to comprehensively investigate factors related to women’s decisions to leave or stay in engineering.2. The researchers compared responses from current engineers with those of engineers who had left <strong>the</strong> field in <strong>the</strong> previous fiveyears (ra<strong>the</strong>r than with those of women who had left at any point in <strong>the</strong>ir careers) to provide similar time frames for comparisonas well as to ensure that recollections were recent enough to be accurate.
Chapter 10.What Can We Do?
98SOLVING THE EQUATIONUnderrepresentation of women in engineeringand computing is a deeply rooted and complexsocial problem. But recent research and real-worldinitiatives have shown that <strong>the</strong>re are ways to reducegender bias, increase <strong>the</strong> perceived and actualsocial relevance of engineering and computing, andultimately increase women’s sense of belonging in<strong>the</strong>se fields. Employers, educators, policy makers,and individuals can all take steps to improve women’srepresentation in engineering and computing.Reduce <strong>the</strong> influence ofgender biasReducing bias against women in engineering andcomputing fields is a society-wide endeavor. Thebest long-term strategy for accomplishing thisgoal is to change cultural stereotypes that lead togender biases—for example, that men are betterthan women at math, science, and <strong>the</strong> o<strong>the</strong>r skillsthat engineers and computing professionals need.Ironically, one way to change <strong>the</strong> operative stereotypesis for a critical mass of women to succeed inengineering and computing occupations (Eagly &Diekman, 2012), which brings us back to squareone.Change implicit biasesImplicit gender biases are more prevalent todaythan explicit gender biases are. While evidence issparse on how to change implicit biases in <strong>the</strong> longterm (Lai et al., 2013), positive role models appearto make a difference (Young, D. M., et al., 2013;Manke & Cohen, 2011; Asgari et al., 2010; Druryet al., 2011). A natural experiment in India, wherea law reserved village council leadership positionsfor women in randomly selected villages, illustratesthis finding. Researchers found that men in villagesthat were required to have female council leadersheld weaker implicit biases associating leadershipwith men than did men living in villages without agender quota (Beaman et al., 2009).Because implicit associations between mathand gender have been shown to be in place byage 7 or 8 (Cvencek, Meltzoff et al., 2011), ourbest chance to influence implicit biases may be toexpose girls and boys to positive female role modelsin engineering and computing early in life (Baronet al., 2014). Changing <strong>the</strong> formation of implicitbiases among children may be an effective approachfor reducing discrimination in <strong>the</strong> long term, buto<strong>the</strong>r tactics are needed to minimize <strong>the</strong> negativeeffects of gender bias in <strong>the</strong> workplace today. Whileeveryone has a role to play, employers occupy positionsof particular influence in this project.Change organizational practicesResearch points to a number of organizationalpractices that can help reduce <strong>the</strong> influence ofgender bias. For example, making job qualificationsclear and applying <strong>the</strong>m evenly to all candidates,basing decisions on objective past performance,being aware of one’s own potential biases, andallowing sufficient time to make in-depth andindividualized evaluations can reduce <strong>the</strong> influenceof gender biases on hiring decisions (Uhlmann& Cohen, 2005; Isaac et al., 2009; Reuben et al.,2014a; Bendick & Nunes, 2012). Implementing<strong>the</strong> following practices can also help reduce genderbias related to both hiring and retaining women inengineering and computing.Remove gender information fromcandidate evaluationsOne approach to reducing <strong>the</strong> influence of biasesin evaluations of o<strong>the</strong>rs is to remove informationabout an individual’s age, race, and gender fromdecision-making contexts. A classic example ofthis approach relates to <strong>the</strong> hiring of professionalmusicians. In 1970 fewer than 10 percent of instrumentalistsin major U.S. symphony orchestras werewomen. Orchestra applicants typically competedfor positions by performing before an auditioncommittee. Because of concerns that selectionsmight be biased in favor of <strong>the</strong> students of certainrenowned teachers, several major U.S. orchestras in<strong>the</strong> 1970s experimented with a new procedure thatinvolved placing a screen between <strong>the</strong> auditioningmusicians and <strong>the</strong> committee, so that judges couldhear but not see <strong>the</strong> applicants. In <strong>the</strong> 20 years following<strong>the</strong> adoption of blind auditions, <strong>the</strong> proportionof women hired by major symphony orchestrasdoubled—from approximately 20 percent toapproximately 40 percent (Goldin & Rouse, 2000).
AAUW 99Removing gender information from <strong>the</strong> applicationprocess allowed for a fairer hiring process. Ofcourse, hiding individuals’ characteristics is notalways feasible, but in certain situations, such aswhen an organization is evaluating résumés of jobapplicants, limiting gender information available toreviewers may help reduce gender discrimination.Hold managers accountableAccountability can encourage managers andrecruiters to rely less on gut instincts, which may bebased on unconscious biases, and exert more effortto ga<strong>the</strong>r relevant information and process <strong>the</strong>information more carefully. When individuals knowthat <strong>the</strong>y will be held accountable for <strong>the</strong>ir actio<strong>nsa</strong>nd decisions, <strong>the</strong>y tend to act in ways that prepare<strong>the</strong>m to justify <strong>the</strong>ir judgments. Stereotypes arean easy and efficient way to make decisions for <strong>the</strong>many professionals who are under time pressureor working on many tasks simultaneously, so whenevaluators are not held accountable for <strong>the</strong>ir judgments,<strong>the</strong>y are likely to rely on stereotype-basedexpectations (Heilman, 2012).Emphasize that gender diversity is a goalResearch has found that a multicultural approach,a vision of diversity that celebrates social andcultural differences (Gutiérrez & Unzueta, 2010),is more effective in reducing bias than a colorblindapproach that ignores different group identities infavor of emphasizing an overarching organizationalidentity (Plaut et al., 2009; Stevens et al., 2008).A multicultural approach is also more effective atcreating environments in which minority groupsare likely to be engaged in <strong>the</strong>ir work and committedto organizational success (Purdie-Vaughns etal., 2008; Plaut et al., 2009).While <strong>the</strong> terms “multicultural” and “colorblind”pertain specifically to racial, ethnic, and culturaldiversity, <strong>the</strong> message is relevant for gender diversityas well. A colorblind approach with respect togender is akin to an organizational culture in whichissues of gender are rarely mentioned. Even if <strong>the</strong>organization emphasizes <strong>the</strong> general importance ofdiversity, <strong>the</strong> importance of gender diversity, in particular,can be obscured because diversity is understoodto mean a number of different things (Bell& Hartmann, 2007; Unzueta et al., 2012). Whenorganizational leaders talk about <strong>the</strong> importanceof diversity, <strong>the</strong>y should be clear that one desiredresult is recruiting and retaining more women.Introduce effective diversity initiativesDiversity does not automatically lead to betteroutcomes. A study of 700 private companies foundthat strategies in which responsibility and accountabilityfor diversity were clear, as when <strong>the</strong>y wereassigned to a diversity committee or to a full-timediversity staff, were <strong>the</strong> most effective. Mentoringand networking interventions were moderatelyeffective as a means to reduce <strong>the</strong> social isolation ofpeople from nondominant groups. Diversity trainingor diversity evaluations that aimed to reducemanagerial bias were <strong>the</strong> least effective (Kalev et al.,2006).None<strong>the</strong>less, some diversity training programshave been shown to reduce gender bias in <strong>the</strong>workplace as well as in higher education settings,including in STEM-specific settings (Moss-Racusin, van der Toorn et al., 2014; Carnes, Devine,Isaac et al., 2012; Carnes, Devine, Manwell et al.,2014; Devine et al., 2012). One study (Catalyst,2012) examined <strong>the</strong> effect of diversity and inclusioneducation at a global engineering company andfound that a diversity training program had a transformativeeffect on those attending <strong>the</strong> program,shifting both <strong>the</strong>ir mindset and behavior, as well ashaving a positive effect on <strong>the</strong> workplace climate.Before attending <strong>the</strong> program, participants werenoncommittal about whe<strong>the</strong>r white men enjoyedprivileged status in U.S. society. Four months after<strong>the</strong> program, however, participants <strong>report</strong>ed anincreased awareness of white male privilege. Theysaid that <strong>the</strong>y were more likely to think criticallyabout social groups, take personal responsibilityfor being inclusive, consider o<strong>the</strong>r points of view,and listen empa<strong>the</strong>tically. Co-workers noted someof <strong>the</strong>se changes as well. Training was especiallysuccessful for participants who were not previouslyconcerned about being prejudiced, a group of peopleparticularly unlikely to recognize and accountfor <strong>the</strong>ir own biases without external intervention.Critical success factors in this training programincluded senior leader participation, a compelling
100SOLVING THE EQUATIONrationale that was clearly and persuasively communicatedbeforehand, ample opportunities forpractice, and an absence of blaming participants forinequality.This study is notable because significantshifts in beliefs are rarely found in attitudinalresearch. People’s beliefs about inequality areparticularly resistant to change (Hafer & Bègue,2005, in Catalyst, 2012). In addition, most of <strong>the</strong>participants were white men, a group of peoplewho have an important role in creating inclusivework environments because <strong>the</strong>y hold a majorityof senior leadership positions in organizations,especially in engineering and technical organizations.Research finds that people tend to value andapprove of diversity-championing efforts when<strong>the</strong>y are performed by white men but disapproveof <strong>the</strong> same efforts when performed by womenand o<strong>the</strong>r underrepresented groups (Hekman &Foo, 2014). The success of this effort points to <strong>the</strong>possibility that diversity training can help organizatio<strong>nsa</strong>chieve cultural change despite <strong>the</strong> finding byKalev and colleagues (2006) that diversity trainingis often ineffective. Clearly, diversity trainingprograms are not created equal, nor is <strong>the</strong> largercontext in <strong>the</strong> organization unimportant.Implement affirmative action policiesAffirmative action refers to “positive steps taken toincrease <strong>the</strong> representation of women and people ofo<strong>the</strong>r underrepresented groups in areas of employment,education, and culture from which <strong>the</strong>y havebeen historically excluded” (Fullinwider, 2014).Affirmative action programs vary in <strong>the</strong>ir prescriptivenessfor employers. At one end of <strong>the</strong> spectrum,opportunity-focused affirmative action programsseek to expand recruitment of underrepresentedgroups or eliminate discrimination from <strong>the</strong> hiringand promotion process, while at <strong>the</strong> o<strong>the</strong>r end,quotas prescribe strong preferential treatment fromemployers (Harrison, D. A., et al., 2006).Examples of affirmative action policies includeplacing job opening announcements where morewomen and people of o<strong>the</strong>r underrepresentedgroups will be likely to see <strong>the</strong>m and conductingoutreach programs designed to attract womenand people of o<strong>the</strong>r underrepresented groups.Federal agencies require that government contractorsdevelop affirmative action programs inwhich <strong>the</strong>y establish goals to reduce or overcomeany instances in which <strong>the</strong>ir workforce has fewerwomen or people of o<strong>the</strong>r underrepresented groupsin a job than would reasonably be expected by <strong>the</strong>iravailability (U.S. Department of Labor, Office ofFederal Contract Compliance Programs, 2002).Affirmative action policies are tools thatrecruiters and managers can use to counteract <strong>the</strong>irgender biases so that <strong>the</strong>y don’t, intentionally orunintentionally, keep women and people of o<strong>the</strong>runderrepresented groups out of engineering andcomputing jobs (Crosby, Iyer, & Sincharoen, 2006;Walton, Spencer et al., 2013). Affirmative actionpolicies are <strong>the</strong> only means of correcting discriminatoryinjustices in <strong>the</strong> United States that do notrely on <strong>the</strong> aggrieved parties coming forward on<strong>the</strong>ir own behalf (Crosby, Iyer, & Sincharoen,2006). This is especially important since manyemployees who are at a disadvantage on <strong>the</strong> basis ofdemographic characteristics, such as gender or race,may not consciously recognize problems related todiscrimination against <strong>the</strong>ir own group (Crosby,Iyer, Clayton et al., 2003).Some critics of affirmative action policiessuggest that such policies lower <strong>the</strong> quality of <strong>the</strong>selected individual or group; however, research onaffirmative action policies related to gender showsthat this is not true. Balafoutas and Sutter (2012)conducted a laboratory experiment where a varietyof affirmative action policies were applied to aseries of competitions. The presence of affirmativeaction policies, such as requiring a woman to beamong <strong>the</strong> winners and giving applicants “points”just because <strong>the</strong>y were women, resulted in nosignificant differences in <strong>the</strong> overall performanceof <strong>the</strong> winner pool as compared to scenarios inwhich affirmative action policies were not used.The researchers found that <strong>the</strong> presence of affirmativeaction policies encouraged more highlyqualified women to enter <strong>the</strong> competition and didnot discourage men from competing, which meansthat although affirmative action policies resultedin more women being among <strong>the</strong> winners, womenwere rarely selected over more-qualified men.Affirmative action policies in this study encouraged
AAUW 101women to throw <strong>the</strong>ir hats into <strong>the</strong> ring, raising <strong>the</strong>competence of <strong>the</strong> applicant pool overall.Along <strong>the</strong> same lines, an examination of <strong>the</strong>effects of political representation quotas in Swedenfound that gender quotas (reserving a certainpercentage of positions for women) increased <strong>the</strong>competence of <strong>the</strong> candidates overall and <strong>the</strong> groupof male candidates in particular, by reducing <strong>the</strong>number of less-qualified men running for politicaloffice (Besley et al., 2013). These studies suggestthat affirmative action policies help level <strong>the</strong> playingfield for applicants without reducing <strong>the</strong> talentthat employers can attract. Such policies are mostsuccessful in workplaces where executives support<strong>the</strong>m and where affirmative action goals and policiesare clearly and persuasively communicated tostaff.Change individual behaviorsFor individual women in engineering and computing,navigating around systemic biases can be difficultif not impossible. Yet research points to certainsurvival skills for women in traditionally masculineenvironments that may be useful, although <strong>the</strong>sestrategies are not a substitute for addressing fundamentalissues of inequality.Research shows that when women in hiringsituations include clear evidence of competency butavoid appearing self-promoting in interviews, <strong>the</strong>yare viewed more positively. In addition, if womenuse only <strong>the</strong>ir first and middle initials instead of<strong>the</strong>ir first name (if it’s clearly a woman’s name)in <strong>the</strong>ir initial application, <strong>the</strong>y may decrease <strong>the</strong>chance that gender bias will influence prospectiveemployers’ evaluations of <strong>the</strong>ir application (seeIsaac et al., 2009, for an overview of this literatureand original sources). Research also showsthat when women present <strong>the</strong>ir contributions asmotivated by <strong>the</strong> interests of <strong>the</strong> group ra<strong>the</strong>rthan <strong>the</strong>mselves, <strong>the</strong>y tend to be perceived morepositively (Lucas & Baxter, 2012; Ridgeway, 1982;Shackelford et al., 1996).Specific to counteracting <strong>the</strong> negative effectsof stereotype threat, approaching interviews aschallenges in which <strong>the</strong> goal is to build skills ra<strong>the</strong>rthan perform well has resulted in women feelingless threatened and performing better (Stout &Dasgupta, 2013). Self-affirmations and reappraisingnegative thoughts—for example, thinkingabout stressful situations in ways that allow oneto feel calm—have also been shown to protectwomen from <strong>the</strong> negative effects of stereotypethreat (Legault et al., 2012; Martens et al., 2006;Schmader, Forbes et al., 2009).Finally, just knowing about stereotype threathas been shown to lessen its effects. Simply understandingthat stereotypes are an external sourceof stress can protect women from <strong>the</strong> harmfuleffects of stereotype threat ( Johns et al., 2005;Dar-Nimrod & Heine, 2006).Make engineering andcomputing more sociallyrelevantStrategically incorporating activities that reflectcommunal values into engineering and computingcurricula and work—and, correspondingly, strategicallycommunicating <strong>the</strong> societal benefits ofengineering and computing to <strong>the</strong> public—is a wayto increase women’s representation in <strong>the</strong>se fields.As Amanda Diekman told AAUW:People do not think STEM fields, in particularengineering and computing, provideopportunities for working with o<strong>the</strong>rs andhelping o<strong>the</strong>rs, so anything that can bedone to incorporate that kind of activity, tohighlight that and draw attention to it, willincrease people’s interest and engagement in<strong>the</strong> domain and <strong>the</strong>ir ability to see <strong>the</strong>mselvesas part of that field.Evidence suggests that highlighting <strong>the</strong> communalaspects of STEM careers increases girls’ interest in<strong>the</strong>se careers (Colvin et al., 2013; Tyler-Wood etal., 2012).Incorporate communal values intocollege curricula and cultureEvidence suggests that incorporating communalvalues into engineering and computing curricula isespecially beneficial for women. Vaz and colleagues(2013) found that Worcester Polytechnic Institute’s
102SOLVING THE EQUATIONlongstanding project-based learning curriculum,centered on getting students out of <strong>the</strong> classroomto solve real-life, open-ended problems, has beenparticularly effective for its female alumni. In asurvey of its engineering graduates during a 38-yearperiod, women were more likely than men to <strong>report</strong>that <strong>the</strong>ir project-based learning experiences hada positive impact across a wide variety of personaland professional development measures, includingunderstanding <strong>the</strong> connections between technologyand society, feeling connected to <strong>the</strong>ir community,and feeling as though <strong>the</strong>y can make a difference.Engineering students who perceived thatethical and social issues were de-emphasized inengineering programs placed a low value on socialconsciousness and public welfare beliefs at <strong>the</strong> endof <strong>the</strong>ir college career. When students perceivedthat <strong>the</strong>ir engineering programs emphasized ethicaland social issues, however, <strong>the</strong>y were more likely tobelieve that social consciousness and o<strong>the</strong>r publicwelfare measures were important (Cech, 2014).While more research is needed on this topic, <strong>the</strong>preliminary lesson seems to be that when engineeringprograms incorporate a clear emphasis on ethicaland social issues, <strong>the</strong>y produce engineers whoprioritize social responsibility.One additional way to attract more women toengineering and computing programs is to coupledegrees in <strong>the</strong>se majors with degrees in o<strong>the</strong>r fieldsthat allow individuals to pursue multiple interests.For example, at Georgia Tech in 2014, 18 percentof bachelor’s degrees in computer science wereawarded to women, while women earned 45 percentof bachelor’s degrees in computational media,a joint degree between computing and <strong>the</strong> schoolof literature, media, and communications (Guzdial,2014).Incorporate communal valuesinto <strong>the</strong> workplaceCan employers do anything to attract individuals,including many women, who value working withand helping people to technical jobs that don’thave clear social contributions? For example, wouldproviding opportunities for mentoring students,junior-level engineers, or technical workers; buildingworkers’ awareness of <strong>the</strong> ultimate beneficiariesof <strong>the</strong>ir research or design efforts; or providing progressivework-family policies help engineering andcomputing workers fulfill <strong>the</strong>ir communal goals?As Diekman told AAUW:If your communal goals are not fulfilled atwork, <strong>the</strong>n extra activities outside of workbecome especially important. Whe<strong>the</strong>r that’sfamily or caregiving or some kind of communityservice or volunteering, communalactivities that are outside of your engineeringcareer might actually have profound implicationsfor your engineering career, becauseif you can fulfill communal goals in o<strong>the</strong>rplaces, <strong>the</strong>n it may not be as important tohave <strong>the</strong>m fulfilled at work.This research suggests that organizations mightbenefit from advancing progressive work-familypolicies or enabling employees to volunteer for asocial cause of <strong>the</strong>ir choice.In addition, individual engineers and computingprofessionals can prioritize <strong>the</strong> availability ofopportunities for communal goal achievementwhen <strong>the</strong>y are looking for jobs. Diekman andher colleagues’ research suggests that consideringpotential for working with and helping o<strong>the</strong>rs,along with technical aspects, when deciding on ajob can help communally oriented engineers andcomputing professionals find more job satisfaction.In tandem, <strong>the</strong> leaders of engineering and computingorganizations should be proactive in clarifying<strong>the</strong> value accorded to communal activities and beparticularly aware of <strong>the</strong> common subtle and overtdevaluing of those activities (Diekman, Weisgramet al., 2015).Cultivate a senseof belongingA sense of belonging has measurable effects onan individual’s physical and mental states. Evenminimal indications of social connectednesscan increase feelings of belonging (Cwir et al.,2011; Walton, Cohen et al., 2012). For women inengineering and computing, having a strong senseof belonging has been found to help alleviate <strong>the</strong>stress that arises from stereotype threat (Shnabel et
AAUW 103al., 2013; Richman et al., 2011; London, Rosenthalet al., 2011; London, Ahlqvist et al., 2014).One way to increase a sense of belongingamong women in computing is to introduce <strong>the</strong>mto computing at an early age. Offering, and possiblyrequiring, computing courses in elementary, middle,and high school can result in more girls feeling that<strong>the</strong>y belong in computing.Creating a genuinely welcoming environmentis a critical step for organizations seeking to attractand retain women and people of o<strong>the</strong>r underrepresentedgroups. Subtle cues can send a message towomen that <strong>the</strong>y don’t belong in an environment.For example, exposure to gender-stereotypical commercialshas been shown to undermine women’saspirations and performance in math and science(Davies et al., 2002). As mentioned in chapter 2, astereotypically “geeky” masculine setting has beenshown to undermine women’s sense of belongingand interest in computing (Cheryan et al., 2009).Changing <strong>the</strong> representations people are exposedto, endorsing a philosophy that explicitly valuesAdvancing women in academicengineering and computingSurvey data and interviews with tenured professorsidentify a sense of community and <strong>the</strong> presence ofa support network as some of <strong>the</strong> most importantfactors in job satisfaction and retention of femaleSTEM faculty (Tyson & Borman, 2010; Young, 2012).The National Science Foundation’s Advance programhas funded <strong>the</strong> efforts of dozens of academicinstitutions to develop systemic approaches andinnovative ways to increase <strong>the</strong> participation andadvancement of women in academic science andengineering careers. Both academic research andstrategies developed by Advance programs, such as<strong>the</strong> Stride program at <strong>the</strong> University of Michigan and<strong>the</strong> WISELI program at <strong>the</strong> University of Wisconsin,consistently identify mentoring and effective, fairleadership as two areas that improve <strong>the</strong> workplaceexperience for female academics in scienceand engineering.<strong>the</strong> social identity of women (Derks et al., 2007),and increasing <strong>the</strong> representation and visibility ofwomen can create an environment in which womenfeel welcome, which can increase motivation, commitment,and persistence (Walton, Spencer et al.,2013).Research has identified a number of strategiesthat women in engineering and computing fieldsuse to increase <strong>the</strong>ir sense of belonging, not allof which are effective. In male-dominated workplaces,<strong>the</strong> culture sometimes encourages womento engage in “discursive positioning” (positioningoneself as an exception to <strong>the</strong> rule) by distancing<strong>the</strong>mselves from o<strong>the</strong>r women (Rhoton, 2011;Faulkner 2009a, 2009b; Servon & Visser, 2011).Individuals may also suppress <strong>the</strong>ir beliefs andopinions to better fit in. While this may workfor a while, self-silencing as a coping mechanismultimately results in individuals feeling alienatedand less motivated, which hinders performance andmay lead to disidentification with <strong>the</strong> work or field(London, Downey et al., 2012). Separating one’swork identity from one’s outside identity is ano<strong>the</strong>rmechanism women may use to fit into engineeringand computing work environments, but that canalso have negative results, such as increased depressionand lowered life satisfaction (von Hippel,Walsh et al., 2011).While <strong>the</strong>se individual strategies are not effective,organizations can take steps to cultivate asense of belonging among women in engineeringand computing. For example, college engineeringor computing department educators can convey <strong>the</strong>message that <strong>the</strong>ir program will require significanteffort from everyone, both women and men.In one study, when undergraduate women heardthat a program would require significant effortfrom everyone, <strong>the</strong>y <strong>report</strong>ed an increased senseof belonging in STEM fields (Smith, J. L., et al.,2013). Along <strong>the</strong> same lines, encouraging a “growthmindset” (a belief in <strong>the</strong> malleability of intelligenceand an awareness that difficulties and challengesare a normal part of earning an engineering orcomputer science degree and working in <strong>the</strong>sefields) has been shown to increase women’s sense ofbelonging (Walton & Cohen, 2007).
104SOLVING THE EQUATIONProviding an environment free from discriminationwhere women have social support is alsoimportant (Richman et al., 2011). Female engineers<strong>report</strong> that having friendly social interactions withco-workers increases <strong>the</strong>ir feeling that <strong>the</strong>y belong(Hatmaker, 2013). Finally, interventions to minimizedisparities and encourage positive relationsbetween groups (women and men, for example)have been shown to help promote engagement andprevent feelings of isolation and devaluation thatcan lead women to feel as if <strong>the</strong>y don’t belong inengineering or computing fields (London, Ahlqvistet al., 2014).In sum, organizations, educators, and individualscan do many things to reduce bias, make engineeringand computing more socially relevant, andencourage a sense of belonging among women.RecommendationsTo increase women’s representation in engineeringand computing occupations, AAUW offers <strong>the</strong>following recommendations, which are based onan extensive review of relevant research and on <strong>the</strong>findings highlighted in this <strong>report</strong>.For employersEmployers are able to influence <strong>the</strong> representationof women in engineering and computing by changing<strong>the</strong> workplace climate and hiring and promotionpractices. For more information, see chapters3 and 4 on bias in hiring and evaluations, chapter 6on <strong>the</strong> importance of communal values, and chapter9 on <strong>the</strong> workplace environment.Maintain good management practicesthat are fair and consistent and thatsupport a healthy work environment• Communicate clear responsibilities, goals, andpaths toward advancement.• Assign employees challenging projects that help<strong>the</strong>m develop and streng<strong>the</strong>n new skills.• Provide training and development opportunitiesfor employees.• Acknowledge and reward employees’contributions.• Ensure that employees have manageable workloadsand are not expected to routinely workexcessive hours.• Provide and encourage <strong>the</strong> use of work-lifebalance support such as on-site daycare, flexiblework schedules, paid parental leave, andtelecommuting.• Provide opportunities for senior technical workersto mentor students or junior-level technicalworkers.• Put in place anti-harassment policies such asthat instituted by <strong>the</strong> Ada Initiative, adainitiative.org/what-we-do/conference-policies.• Work to establish welcoming environmentsthrough inclusive workplace policies.Manage and promote diversity andaffirmative action policies• Ensure that job advertisements, mission statements,and internal communications explicitlyconvey that your organization values diversityand gender inclusiveness.• Assign responsibility for diversity to a diversitycommittee or full-time diversity staff.• Involve men, especially white men, in genderdiversity efforts.• Conduct effective diversity training foremployees.• Monitor your progress in increasing women’srepresentation in technical roles.Reduce <strong>the</strong> negative effects ofgender bias• Make job qualifications clear and apply <strong>the</strong>mevenly to all candidates.• Base hiring decisions on objective past performanceinformation when possible.• Purposely remove gender information fromevaluation scenarios when possible.• Allow sufficient time to make in-depth andindividualized evaluations of applicants.• Ensure that hiring managers and o<strong>the</strong>remployees are aware of <strong>the</strong>ir own potentialgender biases, such as by taking <strong>the</strong> genderscienceImplicit Association Test at implicit.harvard.edu.
AAUW 105• Survey employees to assess <strong>the</strong> level of genderbias within your organization.• Hold managers and recruiters accountable for<strong>the</strong>ir hiring and promotion decisions.Encourage a sense of belonging• Create a welcoming environment for allemployees.• Encourage a supportive, friendly, and respectfulenvironment.• Root out uncivil and undermining behaviors.• Increase <strong>the</strong> number of women at all levels ofmanagement.• Provide opportunities for women to develop asupport network of o<strong>the</strong>r technical women.• Formally recognize necessary nontechnical worksuch as working well with o<strong>the</strong>rs and mentoring—workthat is not male-stereotyped—alongwith technical work.• Be proactive and vocal about management’scommitment to increasing <strong>the</strong> representation oftechnical women in your organization.Facilitate opportunities for employeesto work on projects or issues that aresocially relevant• Pursue projects with clear social impacts wheneverpossible.• Showcase how professionals’ everyday workaligns with <strong>the</strong> societally beneficial outcomesthat are <strong>the</strong> ultimate goals of engineering andtechnology.• Establish social service days where employeesvolunteer in <strong>the</strong>ir communities.For women working inengineering and computingWomen engineering and computing professionalsface challenges navigating stereotypes and genderbiases in environments in which <strong>the</strong>y are often <strong>the</strong>minority. They are also well placed to attract o<strong>the</strong>rwomen to <strong>the</strong>se fields.• Seek a support network. Some possibilitiesinclude participating in a Society of WomenEngineers chapter, <strong>the</strong> Systers e-mail list(hosted by <strong>the</strong> Anita Borg Institute for Womenand Technology), or a women-in-engineering orcomputing group on campus or at work.• Prioritize working in jobs that allow you towork with o<strong>the</strong>rs on socially relevant problemsif you place a high value on communal goals.• Seek opportunities to serve as a role model forgirls and young women considering engineeringand computing careers.• Share with students at all levels how you workwith and help people.For men working in engineeringand computingBecause <strong>the</strong>y make up <strong>the</strong> majority of workers inengineering and computing, men play importantroles in creating <strong>the</strong> workplace climate and inrecruiting and influencing prospective professionals.Importantly, <strong>the</strong> recommendations for increasing<strong>the</strong> representation of women in engineering andcomputing often benefit <strong>the</strong> men in <strong>the</strong>se professio<strong>nsa</strong>s well.• Seek opportunities to serve as a role model forgirls and young women considering engineeringand computing.• Refuse to participate on all-male conferencepanels. Encourage conference organizers torecruit at least one female panelist.• Share with students at all levels how you workwith and help people.For educatorsEducators at all levels influence how studentsperceive <strong>the</strong> fields of engineering and computing,as well as how students view <strong>the</strong>mselves. The followingrecommendations come from <strong>the</strong> literaturereviewed on bias (chapters 2, 3, and 4), stereotypethreat (chapters 2 and 5), and values and careerchoice (chapters 6, 7, and 8).• Spread <strong>the</strong> word that engineering skills andcompetencies are learned, not innate (in o<strong>the</strong>rwords, cultivate a growth mindset). In engineeringand computing classrooms, reduce <strong>the</strong>assumption that technical competence is innateby reinforcing <strong>the</strong> idea that successful engineersor computing professionals are willing
106SOLVING THE EQUATIONto practice to develop <strong>the</strong>ir skills and persistthrough difficulties.• Frame adversity as a common experience foreveryone so that challenging coursework doesnot selectively signal to students that <strong>the</strong>y donot belong in engineering or computing.• Teach students about <strong>the</strong> effects of stereotypethreat to lessen its effects.• Give a broad range of people exposure to computing.Move away from <strong>the</strong> idea that certainpeople (often with strong programming skills)are cut out for computing while o<strong>the</strong>rs are not.• Highlight <strong>the</strong> broad applications of engineeringand computing.• Highlight <strong>the</strong> ways in which engineering andcomputing help people and provide opportunitiesfor working with o<strong>the</strong>rs.• Provide opportunities for girls and youngwomen to interact with women and men withwhom <strong>the</strong>y can identify in engineering andcomputing.• Create welcoming environments for girls inmath, science, engineering, and computing withgender-neutral decor; by endorsing a philosophythat explicitly values <strong>the</strong> social identity ofwomen; and by increasing <strong>the</strong> representationand visibility of girls and women.• Provide girls with opportunities to tinker andbuild confidence and interest in <strong>the</strong>ir design andprogramming abilities.For colleges and universitiesBecause most engineers and computing professionalsare trained in <strong>the</strong>ir professions in institutions ofhigher education, colleges and universities have aspecial role to play in increasing <strong>the</strong> representationof women in engineering and computing.engineering and computing professors• Emphasize <strong>the</strong> social impact of engineering andcomputing work.• Apply concepts that students are learningin class to community needs, incorporatingproject-based learning or service learningcomponents into engineering or computingcurricula.• Apply engineering and computing to realworldproblems.• Emphasize ethical and social issues when teachingengineering and computing.• Encourage a supportive environment in <strong>the</strong>classroom and in <strong>the</strong> program.• Encourage and assist early contact betweenstudents and professionals.• Emphasize <strong>the</strong> wide variety of expertise necessaryto be successful as an engineer or computingprofessional.• Highlight as early as possible <strong>the</strong> different facetsthat make up engineering and computing.Engineering professors• Expand examples beyond those that involvestereotypically male applications such as cars orrockets. The NSF-funded Engage project has acollection of gender-neutral Everyday Examplesin Engineering that professors can use.• Introduce students to experiences in <strong>the</strong> fieldearly in undergraduate coursework to allowstudents to see <strong>the</strong> differences between textbookproblems and <strong>the</strong> creativity and critical thinkingnecessary for actual engineering problemsolving.Computing professors• Split classes by experience, providing studentswith less experience in computing with <strong>the</strong> timeand environment <strong>the</strong>y need to build <strong>the</strong>ir skillsand interest.• Question <strong>the</strong> idea that certain people (oftenwith strong programming skills) are cut out forcomputing while o<strong>the</strong>rs are not.• Send female students (a mix of students interestedin computing and those not consideringcomputing as a major) to <strong>the</strong> Grace HopperCelebration of Women in Computing or similarconferences. Taking even a few students canchange <strong>the</strong> mindset of those students, who can<strong>the</strong>n have a large effect on a program.
AAUW 107Social science professors• Conduct research on how to counteract <strong>the</strong>effects of gender bias and <strong>the</strong> effects of diversityon outcomes.• Conduct more research in field settings, inengineering and computing workplaces, and inclassrooms.Administrators• Require researchers who receive federal funds toparticipate in bias training.• Require all undergraduate students to take atleast one computer science course, no matterwhat <strong>the</strong>ir major.• Provide opportunities for female students inengineering and computing to develop a supportnetwork of o<strong>the</strong>r technical women.• Offer and promote dual-degree programs forstudents interested in engineering or computingwho also have strong interests in o<strong>the</strong>r fields.• Engage in active public relations campaigns thatmake it clear to young women that engineersand technical professionals work cooperativelywith o<strong>the</strong>rs on problems that have impactson <strong>the</strong> well-being of people, for example, byusing <strong>the</strong> National Academy of Engineering’sChanging <strong>the</strong> Conversation materials (2008).For policy makersPolicy makers can help improve <strong>the</strong> representationof women in engineering and computing througheducation programs and research funding, as wellas by ensuring that federally funded programscomply with civil rights laws designed to tackle sexdiscrimination. Congress enacted Title IX to makesure that federal resources are not used to supportdiscriminatory practices in education programsand to provide individual citizens effective protectionagainst such bias. In addition, state and localgovernments can adopt and promote education andworkplace policies that can narrow <strong>the</strong> achievementgap for girls and women in STEM.Federal government• The U.S. Department of Education should issueguidelines for Title IX coordinators that outline<strong>the</strong>ir responsibilities for ensuring equity inSTEM education. The guidelines should coverconcerns from elementary and secondary educationthrough postdoctoral studies and workforcetraining. These guidelines should be broadlydisseminated and publicized.• The executive branch should lead efforts toincrease awareness, comprehensiveness, andtransparency of federal agency Title IX compliancereviews. Such reviews, which all federalagencies should conduct, not only those in <strong>the</strong>Department of Education, are critical to leveragingchange when recipients of federal fundsare found lacking in <strong>the</strong> placement, advancement,and retention of women in STEM disciplines.Compliance reviews and mechanismsfor enforcement of Title IX are available duringpre-award reviews, post-award compliancereviews, and investigations of complaints.• To comply with Title IX, federal agenciesshould ensure that educational institutionsreceiving grant funding or o<strong>the</strong>r financial assistanceprovide policies to maintain safe climatesto prevent sexual harassment (including genderbasedharassment and sexual assault) andnondiscriminatory policies for health insurancebenefits and o<strong>the</strong>r services.• Federal grant processes should allow for flexibilityrelative to academic engineers’ and computerscientists’ life events (such as birth or adoptionof a child), and paid family leave and paid sickdays should be encouraged.• Congress should direct and provide adequatefunding for federal, state, and local agencies toestablish outreach and retention programs at <strong>the</strong>elementary, secondary, and postsecondary levelsto engage women and girls in STEM activities,courses, and career development. For example,Congress should streng<strong>the</strong>n <strong>the</strong> gender-equityprovisions of <strong>the</strong> America Competes Actreauthorization, which authorizes science andtechnology research programs for five years andcontains provisions to support education andtraining aimed at addressing gender discriminationin <strong>the</strong> STEM fields.• Congress should ensure that federal laws, suchas <strong>the</strong> Carl D. Perkins Vocational and Technical
108SOLVING THE EQUATIONEducation Act, that fund and affect STEMeducation and workforce training also holdstates and programs accountable for movingwomen and girls into training that is nontraditionalby gender.• Congress should include in STEM educationlaws provisions for support services, such asdependent care, transportation assistance, careercounseling, tuition assistance, and o<strong>the</strong>r servicesthat allow individuals to successfully completetraining programs. In addition, federallyfunded career guidance and counseling must beprovided to all students and delivered in a fairmanner that ensures that students are receivingunbiased information about high-skill, highwagecareers in nontraditional fields.• Rising above <strong>the</strong> Ga<strong>the</strong>ring Storm, Revisited(National Academy of Sciences et al., 2010),commissioned by Congress, states that U.S.advantages in science and technology have begunto erode and discusses <strong>the</strong> need to improvemath and science education. Unfortunately<strong>the</strong> <strong>report</strong> largely ignores <strong>the</strong> issue of girlsand women in STEM fields. Congress shouldrequest a follow-up <strong>report</strong> on what effectincreasing <strong>the</strong> number of women in STEMfields would have on enabling <strong>the</strong> United Statesto remain a leader in <strong>the</strong> global marketplace.This should illustrate <strong>the</strong> important contributionswomen can make to STEM fields and putweight behind efforts to increase opportunitiesfor women and girls.• Additional funding should be provided to betterunderstand <strong>the</strong> underrepresentation of womenin engineering and computing and to developinterventions that increase <strong>the</strong> representation ofwomen in <strong>the</strong>se fields.State and local governments• States should pass legislation to allow computingclasses taught in secondary education tocount toward graduation requirements.• States should establish high-quality, uniform,and rigorous K–12 education standards, suchas Common Core State Standards and NextGeneration Science Standards or equallyrigorous standards, to ensure that all studentsare taught to <strong>the</strong> same high expectations.• State and local education agencies must be heldaccountable for improving <strong>the</strong> successful accessto, and outcomes of women and girls in, careerand technical education programs, especiallyin programs that are nontraditional for womenand lead to high-skill, high-wage employment.• All education funding should include supportfor teacher training to include recognition ofimplicit gender bias, awareness of stereotypethreat, and ways to promote a growth mindsetin students.• All data regarding STEM study and workforceparticipation collected by state and federalgovernments should be disaggregated and crosstabulatedby gender and race.For parentsParents, like educators, influence how girls perceive<strong>the</strong> fields of engineering and computing as well as<strong>the</strong>ir own abilities and can encourage <strong>the</strong>ir daughtersto develop interest and confidence in <strong>the</strong>sefields. Parents also play an important role in exposing<strong>the</strong>ir children both to <strong>the</strong> fields of engineeringand computing generally and to women in <strong>the</strong>sefields at early ages, when <strong>the</strong>ir implicit biases areforming.• Cultivate a growth mindset in your children.Teach <strong>the</strong>m that <strong>the</strong> brain is like a muscle thatgets stronger and works better <strong>the</strong> more it isexercised. Teach <strong>the</strong>m that passion, dedication,and self-improvement, not simply innate talent,are <strong>the</strong> roads to genius and contribution.• Introduce your daughters to engineering andcomputing.• Encourage your daughters to pursue ma<strong>the</strong>maticsand take calculus.• Introduce your children to women and menwith whom <strong>the</strong>y can identify in engineering andcomputing fields.• Question <strong>the</strong> idea that certain people (oftenwith strong programming skills) are cut out forcomputing while o<strong>the</strong>rs are not.• Provide girls with opportunities to tinker, takethings apart, and put <strong>the</strong>m back toge<strong>the</strong>r.
AAUW 109• Encourage your daughters to play and workwith boys.• Encourage your sons to play and work withgirls.For girlsGirls should learn about engineering and computingso that <strong>the</strong>y can make informed decisions aboutwhe<strong>the</strong>r ei<strong>the</strong>r of <strong>the</strong>se fields is a good fit for <strong>the</strong>irabilities and interests.• Learn about <strong>the</strong> fields of engineering andcomputing. AAUW offers opportunities suchas Tech Trek and Tech Savvy programs thatprovide opportunities for learning about <strong>the</strong>sefields.• Get to know women in engineering andcomputing.• Tinker with things, take things apart, and put<strong>the</strong>m back toge<strong>the</strong>r.• Cultivate a growth mindset. When you challengeyourself, work hard, and learn new things,your brain forms new connections, and overtime you become smarter.• Consider pursuing a dual-degree program incollege, coupling a major in engineering orcomputing with a major in ano<strong>the</strong>r field suchas liberal arts or social science to allow in-depthpursuit of more than one interest.
Appendix
112SOLVING THE EQUATIONFigure A1. Engineering OccupationsFigure A2. Computing OccupationsAerospace engineersAgricultural engineersBiomedical engineersChemical engineersCivil engineersComputer hardware engineersElectrical and electronics engineersElectrical engineersElectronics engineers, except computerEnvironmental engineersIndustrial engineers, including health and safetyHealth and safety engineers, except mining safety engineersand inspectorsIndustrial engineersMarine engineers and naval architectsComputer and information research scientistsComputer and information analystsComputer systems analystsInformation security analystsSoftware developers and programmersComputer programmersSoftware developers, applicationsSoftware developers, systems softwareWeb developersDatabase and systems administrators and network architectsDatabase administratorsNetwork and computer systems administratorsComputer network architectsComputer support specialistsComputer user support specialistsComputer network support specialistsMiscellaneous computer occupationsMaterials engineersMechanical engineersNote: All occupations listed under “Computer Occupations” minor group 15-1100.Source: U.S. Department of Labor, Bureau of Labor Statistics (2009).Mining and geological engineers, including mining safetyengineersNuclear engineersPetroleum engineersMiscellaneous engineersNote: All occupations listed under “Engineers” minor group 17-2000.Source: U.S. Department of Labor, Bureau of Labor Statistics (2009).
AAUW 113FIGURE A3. PAY GAP IN SELECTED ENGINEERING AND COMPUTING OCCUPATIONS, 2013$120,000WomenMenxx% Women's earningsas a percentage ofmen's earnings$100,000Annual earnings$80,000$60,00092%91%87%85%87%87%90% 88%$40,00078%$20, 0000CivilianemployedpopulationComputerprogrammersComputersupportspecialistsSoftwaredevelopersComputersystemsanalystsIndustrialengineersElectricalengineersMechanicalengineersCivilengineersOCCUPATIONSNotes: Includes full-time, year-round civilian population. Not all computing and engineering occupations are included because of large margins of error associated with occupationsin which <strong>the</strong> numbers of workers (especially women) were relatively low. “Software developers” includes applications and systems software. “Industrial engineers” includes healthand safety. “Electrical engineers” includes electronics engineers.Source: L. M. Frehill analysis of U.S. Census Bureau (2014c).
114SOLVING THE EQUATIONFIGURE A4. ASSOCIATE DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1990–201370605058%50%60% 60%48%50%62% 62%52%56%61%58%46%42%Percentage4038%3029%24%21%2012%13%14%15%13%14%1001990 1995 2000 2005 2010 2013EngineeringAll science and engineeringComputingAllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; ma<strong>the</strong>matics and computer science; physical sciences;psychology; social sciences; and engineering.Source: AAUW analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).
AAUW 115FIGURE A5. ENGINEERING AND COMPUTING BACHELOR’S DEGREES,BY RACE/ETHNICITY AND GENDER, 20137,0006,0008%6,184Engineering degreesComputing degreesx% percentage of totalbachelor's degreesawarded in field5,000Number of degrees4,0003,0009%4,0063%2,6509%3,8092,0003%1,4912%1,7231,0001%8552%91000.5% 0.4%0.2% 0.1% 20027667 80WomenMenWomenMenWomenMen(8%) (8%) (10%) (11%) (0.4%) (0.5%)BLACK HISPANIC AMERICAN INDIAN/ALASKA NATIVENotes: Paren<strong>the</strong>ses show <strong>the</strong> percentage of 20–24-year-olds in <strong>the</strong> general population. Racial/ethnic groups include U.S. citizens and permanent residents only. Data based ondegree-granting institutions eligible to participate in Title IV federal financial aid programs.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b).
116SOLVING THE EQUATIONFigure A6a. Engineering Bachelor's Degrees Awarded to Womenat <strong>the</strong> 25 Largest U.S. Engineering Schools, 2012RankInstitutionWomen earningengineering degreesTotal engineeringdegrees conferredPercentage ofengineering degreesawarded to women1 Georgia Institute of Technology, Main Campus 346 1,663 21%2 Pennsylvania State University, Main Campus 248 1,462 17%3 Purdue University, Main Campus 293 1,391 21%4 Texas A&M University, College Station 260 1,346 19%5 University of Illinois, Urbana-Champaign 218 1,289 17%6 North Carolina State University, Raleigh 315 1,270 25%7 University of Michigan, Ann Arbor 276 1,198 23%8 Virginia Polytechnic Institute and State University 208 1,151 18%9 Ohio State University, Main Campus 172 1,128 15%10 University of Texas, Austin 232 1,065 22%11 University of Florida 223 1,062 21%12 California Polytechnic State University, San Luis Obispo 142 995 14%13 Iowa State University 132 898 15%14 University of California, Berkeley 197 883 22%15 Missouri University of Science and Technology 141 804 18%16 University of Minnesota, Twin Cities 155 801 19%17 Arizona State University 142 715 20%18 University of Washington, Seattle Campus 157 704 22%19 University of Maryland, College Park 112 703 16%20 University of California, San Diego 134 702 19%21 Auburn University 106 699 15%22 Rutgers University, New Brunswick 128 683 19%23 University of Central Florida 83 664 13%24 Rensselaer Polytechnic Institute 165 657 25%25 University of Wisconsin, Madison 125 656 19%Note: Ranking includes only institutions that conferred at least 20 engineering bachelor’s degrees in 2012.Source: L. M. Freehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).
AAUW 117Figure A6b. Engineering Bachelor’s Degrees Awarded to Women by U.S.Engineering Schools with <strong>the</strong> Highest Representation of Women, 2012RankInstitutionWomen earningengineering degreesTotal engineeringdegrees conferredPercentage ofengineering degreesawarded to women1 Smith College 22 22 100%2 Prairie View A&M University 69 105 66%3 Harvard University 28 60 47%4 Massachusetts Institute of Technology 201 453 44%5 California Institute of Technology 44 101 44%6 Howard University 24 56 43%7 George Washington University 40 94 43%8 Tuskegee University 18 43 42%9 Franklin W. Olin College of Engineering 28 69 41%10 Rice University 83 215 39%11 Harvey Mudd College 25 65 38%12 Stanford University 125 335 37%13 Humboldt State University 13 35 37%14 Princeton University 64 177 36%15 Yale University 24 67 36%16 Tulane University of Louisiana 16 45 36%17 Northwestern University 108 305 35%18 University of Pennsylvania 100 286 35%19 University of Alaska, Anchorage 20 58 34%20 Hope College 11 32 34%21 Cornell University 196 577 34%22 Tufts University 65 192 34%23 North Carolina A&T State University 61 183 33%24 Eastern Michigan University 15 45 33%25 University of Puerto Rico, Mayagüez 193 580 33%Note: Ranking includes only institutions that conferred at least 20 engineering bachelor’s degrees in 2012.Source: L. M. Freehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).
118SOLVING THE EQUATIONFigure A6c. Computing Bachelor’s Degrees Awarded to Womenat <strong>the</strong> 25 Largest U.S. Computing Schools, 2012RankInstitutionWomen earningcomputing degreesTotal computingdegrees conferredPercentage ofcomputing degreesawarded to women1 University of Phoenix (38 campuses)* 838 3,288 26%2 DeVry University (25 campuses)* 297 1,667 18%3 ITT Technical Institute (86 campuses)* 209 1,512 14%4 Art Institute (33 campuses)* 368 1,009 37%5 University of Maryland, University College 220 806 27%6 Strayer University (17 campuses)* 193 683 28%7 Pennsylvania State University, Main Campus 140 582 24%8 Kaplan University, Davenport Campus* 152 553 27%9 Western Governors University 49 518 9%10 ECPI University* 79 497 16%11 Westwood College (17 campuses)* 93 419 22%12 Full Sail University* 68 375 18%13 Rochester Institute of Technology 27 363 7%14 George Mason University 60 348 17%15 American Intercontinental University Online* 83 320 26%16 Purdue University, Main Campus 46 297 15%17 University of Maryland, Baltimore County 43 278 15%18 Arizona State University 40 261 15%19 American Public University System* 38 258 15%20 University of Maryland, College Park 57 255 22%21 Indiana University, Bloomington 47 234 20%22 Bellevue University 36 207 17%23 Capella University* 55 207 27%24 University of Central Florida 15 204 7%25 Rutgers University, New Brunswick 32 201 16%Note: Asterisks indicate private, for-profit institutions. Ranking includes only institutions that conferred at least 20 computing bachelor’s degrees in 2012.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013)
AAUW 119Figure A6d. Computing Bachelor’s Degrees Awarded to Women at U.S.Computing Schools with <strong>the</strong> Highest Representation of Women, 2012Rank1InstitutionInternational Academy of Design and Technology,Online Campus*Women earningcomputing degreesTotal computingdegrees conferredPercentage ofcomputing degreesawarded to women14 21 67%2 Johnson C. Smith University 15 23 65%3 Westwood College, Chicago Loop* 12 22 55%4 Academy of Art University* 31 58 53%5 Art Institute of California/Argosy University, San Diego* 40 76 53%6Art Institute of California/Argosy University,Sacramento*11 21 52%7 Sou<strong>the</strong>rn University and A&M College 12 23 52%8 Art Institute of Las Vegas* 18 35 51%9 North Carolina Central University 13 28 46%10 North Carolina Wesleyan College 11 24 46%11 Northwest Missouri State University 11 24 46%12 Art Institute of Phoenix* 16 36 44%13 Ohio University, Main Campus 86 195 44%14 Guilford College 11 25 44%15 Indiana Wesleyan University 11 25 44%16 Quinnipiac University 17 39 44%17 Kentucky State University 9 21 43%18 Art Institute of Pittsburgh, Online Division* 20 49 41%19 Harvey Mudd College 13 32 41%202122Art Institute/Miami International University of Artand Design*Art Institute of California/Argosy University,Orange County*Art Institute of California/Argosy University,Inland Empire*17 42 40%19 47 40%16 40 40%23 Brandeis University 8 20 40%24 Art Institute of Seattle* 14 36 39%25 Grand View University 10 26 38%Note: Asterisks indicate private, for-profit institutions. Ranking includes only institutions that conferred at least 20 computing bachelor’s degrees in 2012.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).
120SOLVING THE EQUATIONFIGURE A7. COMPUTING OCCUPATIONS, CURRENT (2012) AND PROJECTED (2022) EMPLOYMENT80070023%20122022xx% change between2012 and 202225%20%60050020%Employment (1,000s)40012%8%30020020%15%7%15%10037%15%0Information securityanalystsComputer systemsanalystsSoftware developers,applicationsSoftware developers,systems softwareComputer user supportspecialistsWeb developersDatabase administratorsComputer and informationresearch scientistsComputer networkarchitectsNetwork and computersystems administratorsComputer programmersComputer networksupport specialistsOCCUPATIONSNotes: Percentages are projected growth in number of workers in each occupation defined as a computer occupation by <strong>the</strong> U.S. Department of Labor, 2012–2022. National growthin computer occupations overall is expected to be 18 percent and in all occupations is expected to be 11 percent.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014d).
AAUW 121FIGURE A8. ENGINEERING OCCUPATIONS, CURRENT (2012) AND PROJECTED (2022) EMPLOYMENT35020%4%20122022xx% changebetween2012 and20223005%5%250Employment (1,000s)2001504%1007% 7%15%5027%26%9%5%1%0BiomedicalPetroleumCivilEnvironmentalMining and geological12% 10%Marine engineers andnaval architectsNuclearComputer hardwareAerospaceIndustrial (including healthand safety)OCCUPATIONSAgricultural5%ChemicalMechanicalElectrical and electronicsEngineers, all o<strong>the</strong>rMaterialsNotes: Percentages are projected percentage growth in number of workers in each occupation included under <strong>the</strong> heading “Engineers” by <strong>the</strong> U.S. Department of Labor, 2012–2022.National growth in engineering occupations overall is expected to be 9 percent and in all occupations is expected to be 11 percent.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014d).
122SOLVING THE EQUATIONFIGURE A9. ENGINEERING FACULTY, BY RANK, GENDER, AND RACE/ETHNICITY, 20131003%7%13%5%11%2%7%5%1%2%805%22%5%24%26%6063%Percentage45%53%40200Assistant professorAssociate professorFull professorURM women Asian women White women URM men Asian menWhite menNotes: Underrepresented minority (URM) includes black, American Indian/Alaska Native, and Hispanic/Latino. Data shown are for <strong>the</strong> 328 participating engineering schools that<strong>report</strong>ed faculty data broken down by gender, race/ethnicity, and rank.Source: L. M. Frehill analysis of data from American Society for Engineering Education (2014).
AAUW 123FIGURE A10. COMPUTING FACULTY, BY RANK, GENDER, AND RACE/ETHNICITY, 20131009%3%7%2%11%0.4%2%12%16%24%2%804%26%5%21%6061%Percentage46%50%40200Assistant professorAssociate professorFull professorURM women Asian women White women URM men Asian menWhite menNotes: Underrepresented minority (URM) includes black, American Indian/Alaska Native, and Hispanic/Latino. Data shown are for <strong>the</strong> 143 participating computer sciencedepartments that <strong>report</strong>ed faculty data broken down by gender, race/ethnicity, and rank. Responses from individuals who indicated that <strong>the</strong>y were multiracial or nonresident alie<strong>nsa</strong>nd those whose ethnicity is unknown or not <strong>report</strong>ed were not included.Source: L. M. Frehill analysis of data from Computing Research Association (2014).
124SOLVING THE EQUATIONFigure A11. Academic Disciplines Included in EngineeringClassificationof InstructionalDisciplinePrograms (CIP) CodeEngineering, general 14.0101Aerospace, aeronautical andastronautical/space engineering14.0201Agricultural engineering 14.0301Architectural engineering 14.0401Bioengineering and biomedicalengineering14.0501Chemical engineering 14.07Civil engineering 14.08Computer engineering, general 14.0901Computer software engineering 14.0903Electrical, electronics andcommunications engineering14.10Environmental/environmental heal<strong>the</strong>ngineering14.1401Mechanical engineering 14.1901Nuclear engineering 14.2301Petroleum engineering 14.2501Systems engineering 14.2701Materials science and engineeringMaterials engineering 14.1801Materials science 40.1001Industrial engineering and managementIndustrial engineering 14.3501Engineering/industrial management 15.1501DisciplineAll o<strong>the</strong>rClassificationof InstructionalPrograms (CIP) CodePre-engineering 14.0102Ceramic sciences and engineering 14.0601Computer engineering, o<strong>the</strong>r 14.0999Engineering mechanics 14.1101Engineering physics/applied physics 14.1201Engineering science 14.1301Metallurgical engineering 14.2001Mining and mineral engineering 14.2101Naval architecture and marineengineering14.2201Ocean engineering 14.2401Textile sciences and engineering 14.2801Polymer/plastics engineering 14.3201Construction engineering 14.3301Forest engineering 14.3401Manufacturing engineering 14.3601Surveying engineering 14.3801Geological/geophysical engineering 14.3901Paper science and engineering 14.4001Electromechanical engineering 14.4101Biochemical engineering 14.4301Engineering chemistry 14.4401Biological/biosystems engineering 14.4501Engineering, o<strong>the</strong>r 14.9999Geographic information science 45.0702Source: U.S. Department of Education, National Center for Education Statistics (2010).
AAUW 125Figure A12. Academic Disciplines Included in ComputingDisciplineClassification of InstructionalPrograms (CIP) CodeComputer and information sciences, general 11.01Computer programming 11.02Data processing 11.03Information science/studies 11.04Computer systems analysis 11.05Computer science 11.07Computer software and media applications 11.08Computer systems networking and telecommunications 11.09Computer/information technology administration and management 11.10Computer and information sciences and support services, o<strong>the</strong>r 11.99Notes: CIP code 11.06 (data entry/microcomputer applications) is <strong>the</strong> only discipline that <strong>the</strong> U.S. Department of Education includes under CIP code11 that is not included here. CIP codes 11.03 and 11.99 are combined for <strong>the</strong> category “computer/information science support services, including dataprocessing” in figure 8.Source: U.S. Department of Education, National Center for Education Statistics (2010).
126SOLVING THE EQUATIONFIGURE A13. MASTER’S DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–2013706058%59%60%60%53%55%5050%50%49%45%43%44%46%4040%38%Percentage32%34%33%3028%29%28%26%29%28%27%22%21%21%23%22%24%2018%15%14%16%11%109%7%1%3%01970 1975 1980 19851990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; ma<strong>the</strong>matics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013), and National Science Foundation, National Center forScience and Engineering Statistics (2014a).
AAUW 127FIGURE A14. DOCTORAL DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–201370605044%45%47%50%45%Percentage403030%34%26%36%28%39%31%36%38%41%2010022%22%16%14%11%10%9%6%4%2%0.4%0%0%1970 1975 1980 198523% 23%20%19%21%17%16%19%18%16%12%9%1990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; ma<strong>the</strong>matics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013) and National Science Foundation, National Center forScience and Engineering Statistics, (2014a).
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AAUW Educational FoundationAAUW Research ReportsRecent AAUW <strong>report</strong>s may be downloaded for free atwww.aauw.org/what-we-do/research.Women in CommunityColleges: Access toSuccess (2013)Graduating to a Pay Gap:The Earnings of Womenand Men One Year afterCollege Graduation (2012)Crossing <strong>the</strong> Line:Sexual Harassmentat School (2011)Why So Few? Womenin Science, Technology,Engineering, andMa<strong>the</strong>matics (2010)Where <strong>the</strong> Girls Are:The Facts about GenderEquity in Education (2008)Behind <strong>the</strong> Pay Gap (2007)Drawing <strong>the</strong> Line: SexualHarassment on Campus(2006)Tenure Denied: Cases ofSex Discrimination inAcademia (2004)
AAUW at Workfor Women and GirlsThe American Association of University Women (AAUW) is <strong>the</strong> nation’s leadingorganization empowering women and girls. Since 1881, AAUW has helped womenmake a difference for <strong>the</strong>mselves and <strong>the</strong>ir communities.AAUW’s mission is to advance equity for women and girls through advocacy, education,philanthropy, and research.ResearchOur rigorous <strong>report</strong>s fuel curricula, legislation,grassroots projects, and best practices.EducationOur programs reach women and girls at every ageand every stage—from K–12 to careers to retirement.PhilanthropyOne of <strong>the</strong> world’s largest funders of women’seducation, AAUW gives $3.7 million a year infellowships and grants—many of which supportwomen in science, technology, engineering, andmath.AdvocacyAAUW’s nonpartisan work keeps women’s progresson track in local, state, national, and global publicpolicy.And AAUW brings training and empowerment to thousands.10,000college women getleadership trainingfrom AAUW each year.11,000girls attend AAUWscience, technology,engineering, and mathprograms every year.1,000member leadersattend our biennialconference.AAUW boasts 170,000 members and supporters, 1,000 local branches (at least one in every state, andwe’re expanding globally), and 800 college/university partner members.Join us.www.aauw.org/joinSupport our work.www.aauw.org/contribute1111 Sixteenth St. NW • Washington, DC 20036 • 202.785.7700 • www.aauw.org
AAUW Board of DirectorsPatricia Fae Ho, AAUW PresidentJulia T. Brown, AAUW Vice PresidentRosalynd C. Homer, AAUW Finance Vice PresidentSandra Camillo, AAUW SecretaryAmy BlackwellKathryn BraemanMalinda GaulCharmen GoehringSally Anne GoodsonEileen HartmannDavid KirkwoodBetsy McDowellRebecca NorlanderPamella ThielPeggy Ryan WilliamsAAUW Executive OfficeLinda D. Hallman, Executive DirectorJill Birdwhistell, Chief Operating OfficerMike Gellman, Chief Financial OfficerMark Hopkins, Chief Strategy OfficerAAUW Research DepartmentCa<strong>the</strong>rine Hill, Vice President of ResearchKathleen Benson, Research AssistantChristianne Corbett, Senior ResearcherAAUW Art, Editorial, and Media DepartmentRebecca Lanning, Vice President of Art, Editorial, and MediaHannah Moulton Belec, Senior Editor/WriterKathryn Bibler, Editor/WriterElizabeth Bolton, Associate Director of Art, Editorial, and MediaKa<strong>the</strong>rine Broendel, Media and Public Relations ManagerMukti Desai, Creative DirectorElizabeth Escobar, Editorial AssistantLisa Goodnight, Senior Media and Public Relations ManagerCasey Riney, Junior DesignerAllison VanKanegan, Senior Graphic DesignerAAUW Development DepartmentCordy Galligan, Vice President of Marketing and Business DevelopmentEllen Root, Vice President of Corporate DevelopmentBethany Imondi, Development AssociateNicole Phillips, Senior Associate of Corporate Development
MissionAAUW advances equity for women and girls throughadvocacy, education, philanthropy, and research.Value PromiseBy joining AAUW, you belong to a community thatbreaks through educational and economic barriersso that all women and girls have a fair chance.Vision StatementAAUW empowers all women and girls to reach <strong>the</strong>ir highest potential.Diversity StatementAAUW values and seeks a diverse membership. There shall be nobarriers to full participation in this organization on <strong>the</strong> basis of gender,race, creed, age, sexual orientation, national origin, disability, or class.
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